{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:56:02Z","timestamp":1780516562183,"version":"3.54.1"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Karlsruher Institut f\u00fcr Technologie (KIT)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Bus Inf Syst Eng"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact\u2019s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators\u2019 specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature.<\/jats:p>","DOI":"10.1007\/s12599-022-00745-z","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T15:26:27Z","timestamp":1648826787000},"page":"707-728","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems"],"prefix":"10.1007","volume":"64","author":[{"given":"Julius Peter","family":"Landwehr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"K\u00fchl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jannis","family":"Walk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Gn\u00e4dig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"745_CR1","unstructured":"Abadi M et\u00a0al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. http:\/\/tensorflow.org\/, software available from http:\/\/tensorflow.org. Accessed 17 Nov 2021"},{"issue":"1","key":"745_CR2","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/S0378-7796(99)00037-1","volume":"53","author":"R Aggarwal","year":"2000","unstructured":"Aggarwal R, Johns A, Jayasinghe J, Su W (2000) An overview of the condition monitoring of overhead lines. Electr Power Syst Res 53(1):15\u201322","journal-title":"Electr Power Syst Res"},{"key":"745_CR3","doi-asserted-by":"crossref","unstructured":"Akhloufi M, Benmesbah N (2014) Outdoor ice accretion estimation of wind turbine blades using computer vision. In: 2014 Canadian conference on computer and robot vision. IEEE, pp 246\u2013253","DOI":"10.1109\/CRV.2014.41"},{"key":"745_CR4","first-page":"923","volume":"13","author":"D Arnott","year":"2012","unstructured":"Arnott D, Pervan G (2012) Design science in decision support systems research: an assessment using the Hevner, March, Park, and Ram guidelines. J Assoc Inf Syst 13:923\u2013949","journal-title":"J Assoc Inf Syst"},{"issue":"11","key":"745_CR5","doi-asserted-by":"publisher","first-page":"27783","DOI":"10.3390\/s151127783","volume":"15","author":"GS Avellar","year":"2015","unstructured":"Avellar GS, Pereira GA, Pimenta LC, Iscold P (2015) Multi-UAV routing for area coverage and remote sensing with minimum time. Sensors 15(11):27783\u201327803","journal-title":"Sensors"},{"key":"745_CR6","unstructured":"Baladi GY, Dawson T, Musunuru G, Prohaska M, Thomas K et\u00a0al (2017) Pavement performance measures and forecasting and the effects of maintenance and rehabilitation strategy on treatment effectiveness. Tech. rep., United States. Federal Highway Administration"},{"issue":"12","key":"745_CR7","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1016\/j.acra.2017.06.008","volume":"24","author":"A Ben-Cohen","year":"2017","unstructured":"Ben-Cohen A, Klang E, Diamant I, Rozendorn N, Raskin SP, Konen E, Amitai MM, Greenspan H (2017) CT image-based decision support system for categorization of liver metastases into primary cancer sites: initial results. Acad Radiol 24(12):1501\u20131509","journal-title":"Acad Radiol"},{"key":"745_CR8","doi-asserted-by":"crossref","unstructured":"Bharati P, Pramanik A (2020) Deep learning techniques\u2014R-CNN to mask R-CNN: a survey. In: Computational intelligence in pattern recognition. Springer, Heidelberg, pp 657\u2013668","DOI":"10.1007\/978-981-13-9042-5_56"},{"issue":"107","key":"745_CR9","first-page":"547","volume":"224","author":"J Bokrantz","year":"2020","unstructured":"Bokrantz J, Skoogh A, Berlin C, Wuest T, Stahre J (2020) Smart maintenance: a research agenda for industrial maintenance management. Int J Prod Econ 224(107):547","journal-title":"Int J Prod Econ"},{"key":"745_CR10","first-page":"1","volume":"1","author":"E Brynjolfsson","year":"2017","unstructured":"Brynjolfsson E, Mcafee A (2017) The business of artificial intelligence. Harvard Bus Rev 1:1\u201320","journal-title":"Harvard Bus Rev"},{"key":"745_CR11","doi-asserted-by":"publisher","DOI":"10.1142\/8893","volume-title":"Theory of knowledge: structures and processes","author":"M Burgin","year":"2016","unstructured":"Burgin M (2016) Theory of knowledge: structures and processes. World Scientific, Singapore"},{"key":"745_CR12","doi-asserted-by":"crossref","unstructured":"Chandra L, Seidel S, Gregor S (2015) Prescriptive knowledge in is research: Conceptualizing design principles in terms of materiality, action, and boundary conditions. In: Proceedings of the annual Hawaii international conference on system sciences. pp 4039\u20134048","DOI":"10.1109\/HICSS.2015.485"},{"key":"745_CR13","unstructured":"Chatterjee S, Brendel AB, Lichtenberg S (2018) Smart infrastructure monitoring: Development of a decision support system for vision-based road crack detection. In: International conference on information systems 2018, ICIS 2018 (October)"},{"issue":"113","key":"745_CR14","first-page":"234","volume":"130","author":"N Chaudhuri","year":"2020","unstructured":"Chaudhuri N, Bose I (2020) Exploring the role of deep neural networks for post-disaster decision support. Decis Support Syst 130(113):234","journal-title":"Decis Support Syst"},{"key":"745_CR15","unstructured":"Chollet F et\u00a0al (2015) Keras. https:\/\/keras.io. Accessed 17 Nov 2021"},{"key":"745_CR16","doi-asserted-by":"crossref","unstructured":"Clobridge A (2010) 5-Metadata. In: Clobridge A (ed) Building a digital repository program with limited resources, Chandos information professional series. pp 85\u2013109","DOI":"10.1016\/B978-1-84334-596-1.50005-5"},{"issue":"4","key":"745_CR17","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s001380050104","volume":"11","author":"D Comaniciu","year":"1999","unstructured":"Comaniciu D, Meer P, Foran DJ (1999) Image-guided decision support system for pathology. Mach Vis Appl 11(4):213\u2013224","journal-title":"Mach Vis Appl"},{"issue":"3","key":"745_CR18","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1046\/j.1365-2648.1997.t01-25-00999.x","volume":"26","author":"IT Coyne","year":"1997","unstructured":"Coyne IT (1997) Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? J Adv Nurs 26(3):623\u2013630","journal-title":"J Adv Nurs"},{"issue":"3","key":"745_CR19","doi-asserted-by":"publisher","first-page":"319","DOI":"10.2307\/249008","volume":"13","author":"FD Davis","year":"1989","unstructured":"Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319\u2013340","journal-title":"MIS Q"},{"key":"745_CR20","doi-asserted-by":"publisher","first-page":"687","DOI":"10.12720\/jcm.9.9.687-692","volume":"9","author":"C Deng","year":"2014","unstructured":"Deng C, Wang S, Huang Z, Tan Z, Liu J (2014) Unmanned aerial vehicles for power line inspection: a cooperative way in platforms and communications. JCM 9:687\u2013692","journal-title":"JCM"},{"issue":"1","key":"745_CR21","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TITS.2016.2568758","volume":"18","author":"X Gibert","year":"2017","unstructured":"Gibert X, Patel VM, Chellappa R (2017) Deep multitask learning for railway track inspection. IEEE Trans Intell Transp Syst 18(1):153\u2013164. https:\/\/doi.org\/10.1109\/TITS.2016.2568758","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"745_CR22","unstructured":"Gleave SD, Frisoni R, Dionori F, Casullo L, Vollath C, Devenish L, Spano F, Sawicki T, Carl S, Lidia R, Neri J, Silaghi R, Stanghellini A (2014) EU road surfaces: economic and safety impact of the lack of regular road maintenance\u2013study. Tech. rep"},{"key":"745_CR23","volume-title":"Digital image processing","author":"R Gonzalez","year":"2018","unstructured":"Gonzalez R, Woods R (2018) Digital image processing. Pearson, London"},{"key":"745_CR24","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http:\/\/www.deeplearningbook.org, Accessed 17 Nov 2021"},{"key":"745_CR25","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.procir.2015.02.093","volume":"30","author":"M Gopalakrishnan","year":"2015","unstructured":"Gopalakrishnan M, Bokrantz J, Ylip\u00e4\u00e4 T, Skoogh A (2015) Planning of maintenance activities\u2014a current state mapping in industry. Procedia CIRP 30:480\u2013485","journal-title":"Procedia CIRP"},{"key":"745_CR26","doi-asserted-by":"publisher","first-page":"337","DOI":"10.25300\/MISQ\/2013\/37.2.01","volume":"37","author":"S Gregor","year":"2013","unstructured":"Gregor S, Hevner A (2013) Positioning and presenting design science research for maximum impact. MIS Q 37:337\u2013356","journal-title":"MIS Q"},{"issue":"5","key":"745_CR27","first-page":"312","volume":"8","author":"S Gregor","year":"2007","unstructured":"Gregor S, Jones D (2007) The anatomy of a design theory. J Assoc Inf Syst 8(5):312\u2013335","journal-title":"J Assoc Inf Syst"},{"key":"745_CR28","unstructured":"Griebel M, D\u00fcrr A, Stein N (2019) Applied image recognition: guidelines for using deep learning models in practice. In: Proceedings of the 14th international conference on Wirtschaftsinformatik"},{"key":"745_CR29","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015a) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"745_CR101","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015b)\u00a0Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In:\u00a0IEEE International Conference on Computer Vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"745_CR30","first-page":"4","volume":"19","author":"A Hevner","year":"2007","unstructured":"Hevner A (2007) A three cycle view of design science research. Scand J Inf Syst 19:4","journal-title":"Scand J Inf Syst"},{"key":"745_CR31","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-1-4419-5653-8_2","volume-title":"Design science research in information systems","author":"A Hevner","year":"2010","unstructured":"Hevner A, Chatterjee S (2010) Design science research in information systems, vol 28. Springer, Berlin, pp 9\u201322"},{"key":"745_CR32","doi-asserted-by":"publisher","first-page":"75","DOI":"10.2307\/25148625","volume":"28","author":"AR Hevner","year":"2004","unstructured":"Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28:75","journal-title":"MIS Q"},{"issue":"1","key":"745_CR33","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1049\/oap-cired.2017.0290","volume":"2017","author":"RZ Homma","year":"2017","unstructured":"Homma RZ, Cosentino A, Szymanski C (2017) Autonomous inspection in transmission and distribution power lines\u2013methodology for image acquisition by means of unmanned aircraft system and its treatment and storage. CIRED Open Access Proc J 2017(1):965\u2013967","journal-title":"CIRED Open Access Proc J"},{"key":"745_CR34","doi-asserted-by":"crossref","unstructured":"Huang L, Xu D, Zhai D (2018) Research and design of space-sky-ground integrated transmission line inspection platform based on artificial intelligence. In: IEEE conference on energy internet and energy system integration (EI2). pp 1\u20134","DOI":"10.1109\/EI2.2018.8582405"},{"key":"745_CR35","first-page":"1","volume":"2018","author":"X Hui","year":"2018","unstructured":"Hui X, Bian J, Zhao X, Tan M (2018) Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection. Int J Adv Robot Syst 2018:1\u201315","journal-title":"Int J Adv Robot Syst"},{"key":"745_CR36","doi-asserted-by":"publisher","first-page":"3014","DOI":"10.3390\/s19133014","volume":"19","author":"B Jalil","year":"2019","unstructured":"Jalil B, Leone G, Martinelli M, Moroni D, Pascali M, Berton A (2019) Fault detection in power equipment via an unmanned aerial system using multi modal data. Sens 19:3014","journal-title":"Sens"},{"key":"745_CR37","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.trc.2018.07.007","volume":"95","author":"A Jamshidi","year":"2018","unstructured":"Jamshidi A, Hajizadeh S, Su Z, Naeimi M, N\u00fa\u00f1ez A, Dollevoet R, Schutter BD, Li Z (2018) A decision support approach for condition-based maintenance of rails based on big data analysis. Transp Res Part C Emerg Technol 95:185\u2013206","journal-title":"Transp Res Part C Emerg Technol"},{"key":"745_CR38","doi-asserted-by":"publisher","unstructured":"Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Market. https:\/\/doi.org\/10.1007\/s12525-021-00475-2. Accessed 17 Nov 2021","DOI":"10.1007\/s12525-021-00475-2"},{"issue":"7","key":"745_CR39","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","volume":"20","author":"AK Jardine","year":"2006","unstructured":"Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483\u20131510","journal-title":"Mech Syst Signal Process"},{"key":"745_CR40","doi-asserted-by":"crossref","unstructured":"Jones D (2005) Power line inspection\u2014a UAV concept. p\u00a08","DOI":"10.1049\/ic:20050472"},{"issue":"1","key":"745_CR42","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TPWRD.2009.2035427","volume":"25","author":"J Katrasnik","year":"2010","unstructured":"Katrasnik J, Pernus F, Likar B (2010) A survey of mobile robots for distribution power line inspection. IEEE Trans Power Deliv 25(1):485\u2013493","journal-title":"IEEE Trans Power Deliv"},{"issue":"1","key":"745_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/2945.981847","volume":"8","author":"DA Keim","year":"2002","unstructured":"Keim DA (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Graph 8(1):1\u20138","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"745_CR44","first-page":"118","volume-title":"Qualitative methods and analysis in organizational research: a practical guide","author":"N King","year":"1998","unstructured":"King N (1998) Template analysis. In: Symon G, Cassell C (eds) Qualitative methods and analysis in organizational research: a practical guide. Sage Publications Ltd., pp 118\u2013134"},{"issue":"3","key":"745_CR45","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.ejor.2019.09.018","volume":"281","author":"M Kraus","year":"2020","unstructured":"Kraus M, Feuerriegel S, Oztekin A (2020) Deep learning in business analytics and operations research: models, applications and managerial implications. Eur J Oper Res 281(3):628\u2013641","journal-title":"Eur J Oper Res"},{"issue":"1","key":"745_CR46","first-page":"46","volume":"48","author":"N K\u00fchl","year":"2021","unstructured":"K\u00fchl N, Hirt R, Baier L, Schmitz B, Satzger G (2021) How to conduct rigorous supervised machine learning in information systems research: the supervised machine learning report card. Commun Assoc Inf Syst 48(1):46","journal-title":"Commun Assoc Inf Syst"},{"key":"745_CR47","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u201344","journal-title":"Nature"},{"key":"745_CR48","doi-asserted-by":"crossref","unstructured":"Li D, Wang X (2019) The future application of transmission line automatic monitoring and deep learning technology based on vision. In: 2019 IEEE 4th international conference on cloud computing and big data analysis (ICCCBDA). pp 131\u2013137","DOI":"10.1109\/ICCCBDA.2019.8725702"},{"key":"745_CR49","doi-asserted-by":"publisher","first-page":"38448","DOI":"10.1109\/ACCESS.2020.2974798","volume":"8","author":"H Liang","year":"2020","unstructured":"Liang H, Zuo C, Wei W (2020) Detection and evaluation method of transmission line defects based on deep learning. IEEE Access 8:38448\u201338458","journal-title":"IEEE Access"},{"key":"745_CR50","first-page":"740","volume":"2014","author":"TY Lin","year":"2014","unstructured":"Lin TY et al (2014) Microsoft coco: common objects in context. Comput Vis ECCV 2014:740\u2013755","journal-title":"Comput Vis ECCV"},{"key":"745_CR51","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: single shot multibox detector. LNCS, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"745_CR52","unstructured":"Liu X, Miao X, Jiang H, Chen J (2020) Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology. CoRR arXiv:2003.09802"},{"issue":"4","key":"745_CR53","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0167-9236(94)00041-2","volume":"15","author":"ST March","year":"1995","unstructured":"March ST, Smith GF (1995) Design and natural science research on information technology. Decis Support Syst 15(4):251\u2013266","journal-title":"Decis Support Syst"},{"key":"745_CR54","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","volume":"119","author":"L Matikainen","year":"2016","unstructured":"Matikainen L, Lehtom\u00e4ki M, Ahokas E, Hyypp\u00e4 J, Karjalainen M, Jaakkola A, Kukko A, Heinonen T (2016) Remote sensing methods for power line corridor surveys. J Photogram Remote Sens 119:10\u201331","journal-title":"J Photogram Remote Sens"},{"key":"745_CR55","unstructured":"Mayring P (1991) Qualitative inhaltsanalyse. In: Flick U, Kardoff Ev, Keupp H, Rosenstiel Lv, Wolff S (eds) Handbuch qualitative Forschung: Grundlagen, Konzepte, Methoden und Anwendungen. pp 209\u2013213"},{"key":"745_CR56","first-page":"799","volume":"16","author":"H Meth","year":"2015","unstructured":"Meth H, Mueller B, Maedche A (2015) Designing a requirement mining system. J Assoc Inf Syst 16:799\u2013837","journal-title":"J Assoc Inf Syst"},{"key":"745_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller T (2019) Explanation in artificial intelligence: insights from the social sciences. Artif Intell 267:1\u201338","journal-title":"Artif Intell"},{"key":"745_CR58","doi-asserted-by":"crossref","unstructured":"Mirall\u00e8s F, Pouliot N, Montambault S (2014) State-of-the-art review of computer vision for the management of power transmission lines. In: Proceedings of the 2014 3rd international conference on applied robotics for the power industry. pp 1\u20136","DOI":"10.1109\/CARPI.2014.7030068"},{"key":"745_CR59","doi-asserted-by":"crossref","unstructured":"Montambault S, Beaudry J, Toussaint K, Pouliot N (2010) On the application of VTOL UAVs to the inspection of power utility assets. In: 2010 1st international conference on applied robotics for the power industry. pp 1\u20137","DOI":"10.1109\/CARPI.2010.5624443"},{"key":"745_CR60","doi-asserted-by":"crossref","unstructured":"Nazerdeylami A, Majidi B, Movaghar A (2019) Smart coastline environment management using deep detection of manmade pollution and hazards. In: 2019 5th conference on knowledge based engineering and innovation (KBEI). pp 332\u2013337","DOI":"10.1109\/KBEI.2019.8735012"},{"key":"745_CR61","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ijepes.2017.12.016","volume":"99","author":"VN Nguyen","year":"2018","unstructured":"Nguyen VN, Jenssen R, Roverso D (2018) Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int J Electr Power Energ Syst 99:107\u2013120","journal-title":"Int J Electr Power Energ Syst"},{"key":"745_CR62","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPETS.2018.2881429","volume":"6","author":"V Nguyen","year":"2019","unstructured":"Nguyen V, Jenssen R, Roverso D (2019) Intelligent monitoring and inspection of power line components powered by UAVs and deep learning. IEEE Power Energ Technol Syst J 6:11\u201321","journal-title":"IEEE Power Energ Technol Syst J"},{"key":"745_CR41","unstructured":"Nicola H, Kappeler U-P, Daniela N, Thomas S, Matthias G (2005) Benefits of integrating meta data into a context model. In: Third IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE, pp 25\u201329"},{"key":"745_CR64","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.procir.2013.09.041","volume":"12","author":"A Pagnano","year":"2013","unstructured":"Pagnano A, H\u00f6pf M, Teti R (2013) A roadmap for automated power line inspection. Maintenance and repair. Procedia CIRP 12:234\u2013239","journal-title":"Procedia CIRP"},{"key":"745_CR65","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"745_CR66","doi-asserted-by":"crossref","unstructured":"Pernebayeva D, James AP (2020) Deep-learning-based approach for outdoor electrical insulator inspection. pp 81\u201388","DOI":"10.1007\/978-3-030-14524-8_6"},{"key":"745_CR67","doi-asserted-by":"publisher","first-page":"135","DOI":"10.25103\/jestr.095.21","volume":"9","author":"PS Prasad","year":"2016","unstructured":"Prasad PS, Rao BP, Ece UJKAI (2016) Review on machine vision based insulator inspection systems for power distribution system. J Eng Sci Technol Rev 9:135\u2013141","journal-title":"J Eng Sci Technol Rev"},{"key":"745_CR68","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.compeleceng.2019.08.001","volume":"78","author":"RM Prates","year":"2019","unstructured":"Prates RM, Cruz R, Marotta AP, Ramos RP, Filho EFS, Cardoso JS (2019) Insulator visual non-conformity detection in overhead power distribution lines using deep learning. Comput Electr Eng 78:343\u2013355","journal-title":"Comput Electr Eng"},{"issue":"3","key":"745_CR69","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/TITS.2015.2482222","volume":"17","author":"M Quintana","year":"2016","unstructured":"Quintana M, Torres J, Men\u00e9ndez JM (2016) A simplified computer vision system for road surface inspection and maintenance. IEEE Trans Intell Transp Syst 17(3):608\u2013619. https:\/\/doi.org\/10.1109\/TITS.2015.2482222","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"745_CR70","doi-asserted-by":"crossref","unstructured":"Rafael\u00a0Padilla SLN, da\u00a0Silva EAB (2020) Survey on performance metrics for object-detection algorithms. In: 2020 International conference on systems, signals and image processing (IWSSIP)","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"745_CR71","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-CNN: towards real-time object detection with region proposal networks"},{"issue":"1","key":"745_CR72","first-page":"1","volume":"30","author":"M Ren","year":"2020","unstructured":"Ren M, Vu HQ, Li G, Law R (2020) Large-scale comparative analyses of hotel photo content posted by managers and customers to review platforms based on deep learning: implications for hospitality marketers. J Hosp Market Manag 30(1):1\u201324","journal-title":"J Hosp Market Manag"},{"key":"745_CR73","doi-asserted-by":"crossref","unstructured":"Rufino J, Alam M, Ferreira J, Rehman A, Tsang KF (2017) Orchestration of containerized microservices for IIoT using docker. In: 2017 IEEE international conference on industrial technology (ICIT). IEEE, pp 1532\u20131536","DOI":"10.1109\/ICIT.2017.7915594"},{"issue":"3","key":"745_CR74","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"745_CR75","doi-asserted-by":"publisher","first-page":"101283","DOI":"10.1109\/ACCESS.2019.2931144","volume":"7","author":"C Sampedro P\u00e9rez","year":"2019","unstructured":"Sampedro P\u00e9rez C, Rodriguez-Vazquez J, Rodr\u00edguez Ramos A, Carrio A, Campoy P (2019) Deep learning-based system for automatic recognition and diagnosis of electrical insulator strings. IEEE Access 7:101283\u2013101308","journal-title":"IEEE Access"},{"key":"745_CR76","doi-asserted-by":"crossref","unstructured":"Schumann A, Mood N, Mungofa P, MacEachern C, Zaman Q, Esau T (2019) Detection of three fruit maturity stages in wild blueberry fields using deep learning artificial neural networks. In: 2019 American society of agricultural and biological engineers annual international meeting","DOI":"10.13031\/aim.201900533"},{"key":"745_CR77","first-page":"1","volume":"141","author":"M Schwarz","year":"2018","unstructured":"Schwarz M, Drudi D (2018) Workplace hazards facing line installers and repairers. Mon Lab Rev 141:1\u201313","journal-title":"Mon Lab Rev"},{"issue":"9","key":"745_CR78","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.1177\/0954405415601640","volume":"231","author":"S Selcuk","year":"2017","unstructured":"Selcuk S (2017) Predictive maintenance, its implementation and latest trends. Proc Inst Mech Eng Part B J Eng Manuf 231(9):1670\u20131679. https:\/\/doi.org\/10.1177\/0954405415601640","journal-title":"Proc Inst Mech Eng Part B J Eng Manuf"},{"issue":"1","key":"745_CR79","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/S0167-9236(02)00208-7","volume":"37","author":"A Sen","year":"2004","unstructured":"Sen A (2004) Metadata management: past, present and future. Decis Support Syst 37(1):151\u2013173","journal-title":"Decis Support Syst"},{"key":"745_CR80","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.5370\/JEET.2016.11.5.1412","volume":"11","author":"KH Seok","year":"2016","unstructured":"Seok KH, Kim YS (2016) A state of the art of power transmission line maintenance robots. J Electr Eng Technol 11:1412\u20131422","journal-title":"J Electr Eng Technol"},{"key":"745_CR81","doi-asserted-by":"publisher","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","volume":"7","author":"H Shakhatreh","year":"2019","unstructured":"Shakhatreh H, Sawalmeh AH, Al-Fuqaha A, Dou Z, Almaita E, Khalil I, Othman NS, Khreishah A, Guizani M (2019) Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7:48572\u201348634","journal-title":"IEEE Access"},{"issue":"4","key":"745_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/en12040676","volume":"12","author":"AS Shihavuddin","year":"2019","unstructured":"Shihavuddin AS, Chen X, Fedorov V, Christensen AN, Riis NAB, Branner K, Dahl AB, Paulsen RR (2019) Wind turbine surface damage detection by deep learning aided drone inspection analysis. Energies 12(4):1\u201315","journal-title":"Energies"},{"key":"745_CR83","doi-asserted-by":"crossref","unstructured":"Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings IEEE symposium on visual languages. pp 336\u2013343","DOI":"10.1109\/VL.1996.545307"},{"key":"745_CR84","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, conference track proceedings"},{"issue":"2","key":"745_CR85","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.eng.2018.11.030","volume":"5","author":"BF Spencer","year":"2019","unstructured":"Spencer BF, Hoskere V, Narazaki Y (2019) Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering 5(2):199\u2013222","journal-title":"Engineering"},{"key":"745_CR86","doi-asserted-by":"publisher","DOI":"10.4324\/9780429042522-10","volume-title":"Computer vision: algorithms and applications","author":"R Szeliski","year":"2010","unstructured":"Szeliski R (2010) Computer vision: algorithms and applications. Springer, Heidelberg. https:\/\/doi.org\/10.4324\/9780429042522-10"},{"key":"745_CR87","doi-asserted-by":"crossref","unstructured":"Takaya K, Ohta H, Kroumov V, Shibayama K, Nakamura M (2019) Development of UAV system for autonomous power line inspection. In: International conference on system theory, control and computing. pp 762\u2013767","DOI":"10.1109\/ICSTCC.2019.8885596"},{"key":"745_CR88","doi-asserted-by":"crossref","unstructured":"Toth J, Gilpin-Jackson A (2010) Smart view for a smart grid\u2014unmanned aerial vehicles for transmission lines. In: 2010 1st international conference on applied robotics for the power industry. pp 1\u20136","DOI":"10.1109\/CARPI.2010.5624465"},{"issue":"5","key":"745_CR89","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1002\/rob.20295","volume":"26","author":"K Toussaint","year":"2009","unstructured":"Toussaint K, Pouliot N, Montambault S (2009) Transmission line maintenance robots capable of crossing obstacles: state-of-the-art review and challenges ahead. J Field Robot 26(5):477\u2013499","journal-title":"J Field Robot"},{"key":"745_CR90","unstructured":"Turban E, Sharda R, Delen D (2010) Decision support and business intelligence systems, 9th edn. USA"},{"issue":"1","key":"745_CR91","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1057\/ejis.2014.36","volume":"25","author":"J Venable","year":"2016","unstructured":"Venable J, Pries-Heje J, Baskerville R (2016) Feds: a framework for evaluation in design science research. Eur J Inf Syst 25(1):77\u201389","journal-title":"Eur J Inf Syst"},{"key":"745_CR92","unstructured":"vom Brocke J, Simons A, Niehaves B, Riemer K, Plattfaut R, Cleven A (2009) Reconstructing the giant: on the importance of rigour in documenting the literature search process. In: ECIS 2009 proceedings"},{"key":"745_CR93","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1287\/isre.3.1.36","volume":"3","author":"JG Walls","year":"1992","unstructured":"Walls JG, Widmeyer GR, El Sawy OA (1992) Building an information system design theory for vigilant EIS. Inf Syst Res 3:36\u201359","journal-title":"Inf Syst Res"},{"key":"745_CR94","first-page":"xiii","volume":"26","author":"J Webster","year":"2002","unstructured":"Webster J, Watson R (2002) Analyzing the past to prepare for the future: Writing a literature reviewd. MIS Q 26:xiii\u2013xxiii","journal-title":"MIS Q"},{"key":"745_CR95","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.engappai.2019.01.008","volume":"80","author":"X Wei","year":"2019","unstructured":"Wei X, Yang Z, Liu Y, Wei D, Jia L, Li Y (2019) Railway track fastener defect detection based on image processing and deep learning techniques: a comparative study. Eng Appl Artif Intell 80:66\u201381. https:\/\/doi.org\/10.1016\/j.engappai.2019.01.008","journal-title":"Eng Appl Artif Intell"},{"key":"745_CR96","first-page":"470","volume":"17","author":"R Winter","year":"2008","unstructured":"Winter R (2008) Design science research in Europe. EJIS 17:470\u2013474","journal-title":"EJIS"},{"key":"745_CR97","unstructured":"Xie Y, Noguchi R, Ahamed T (2020) Deep learning and multiple sensors data acquisition system for real-time decision analysis in agriculture using unmanned aerial vehicle. EasyChair Preprint no. 2690"},{"issue":"8","key":"745_CR98","doi-asserted-by":"publisher","first-page":"10097","DOI":"10.1007\/s11042-018-6610-4","volume":"78","author":"Y Yang","year":"2019","unstructured":"Yang Y, Wang L, Wang Y, Mei X (2019) Insulator self-shattering detection: a deep convolutional neural network approach. Multimed Tools Appl 78(8):10097\u201310112","journal-title":"Multimed Tools Appl"},{"key":"745_CR99","doi-asserted-by":"publisher","first-page":"35316","DOI":"10.1109\/ACCESS.2018.2846293","volume":"6","author":"Y Zhai","year":"2018","unstructured":"Zhai Y, Chen R, Yang Q, Li X, Zhao Z (2018) Insulator fault detection based on spatial morphological features of aerial images. IEEE Access 6:35316\u201335326","journal-title":"IEEE Access"},{"key":"745_CR100","doi-asserted-by":"crossref","unstructured":"Zhu X, Kong L, Wang G, Hu Z, Li S (2018) Multi-size object detection assisting fault diagnosis of power systems based on improved cascaded faster R-CNNs. In: Jiang X, Hwang JN (eds) Tenth international conference on digital image processing (ICDIP 2018), international society for optics and photonics, vol 10806. pp 342\u2013351","DOI":"10.1117\/12.2503064"}],"container-title":["Business &amp; Information Systems Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-022-00745-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12599-022-00745-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12599-022-00745-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T10:49:37Z","timestamp":1670582977000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12599-022-00745-z"}},"subtitle":["Evidence From Power Line Maintenance Decision-Making"],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":100,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["745"],"URL":"https:\/\/doi.org\/10.1007\/s12599-022-00745-z","relation":{},"ISSN":["2363-7005","1867-0202"],"issn-type":[{"value":"2363-7005","type":"print"},{"value":"1867-0202","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]},"assertion":[{"value":"6 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}