{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T05:45:05Z","timestamp":1763963105721,"version":"3.45.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031766039"},{"type":"electronic","value":"9783031766046"}],"license":[{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-76604-6_6","type":"book-chapter","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:16:37Z","timestamp":1731716197000},"page":"74-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Uncertainty-Driven ScaledYOLOv4 for Open-Pit Mining Helmet Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7343-1319","authenticated-orcid":false,"given":"Roger","family":"Calle","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2463-0301","authenticated-orcid":false,"given":"Eduardo","family":"Aguilar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,17]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"ANANI, A., Risso, N., Nyaaba, W., Tenorio, V.: Application of machine learning in mine safety: a state-of-the-art review. Available at SSRN 4314075 (2022)","DOI":"10.2139\/ssrn.4314075"},{"key":"6_CR2","unstructured":"Balty, I., Mayer, A.: Head protection - ilo encyclopedia of occupational health and safety [internet] (2011). https:\/\/www.iloencyclopaedia.org\/part-iv-66769\/personal-protection-59388\/item\/689-head-protection"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Calle\u00a0Quispe, R.M., Aghaei\u00a0Gavari, M., Aguilar\u00a0Torres, E.: Hacia una detecci\u00f3n precisa de cascos de seguridad en tiempo real a trav\u00e9s de un m\u00e9todo basado en el aprendizaje profundo. Ingeniare. Revista chilena de ingenier\u00eda 31 (2023)","DOI":"10.4067\/S0718-33052023000100212"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Choi, J., Chun, D., Kim, H., Lee, H.J.: Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE\/CVF Int. Conf. on Computer Vision, pp. 502\u2013511 (2019)","DOI":"10.1109\/ICCV.2019.00059"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"De\u00a0Silva, C.W.: Intelligent control: fuzzy logic applications. CRC press (2018)","DOI":"10.1201\/9780203750513"},{"key":"6_CR6","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: Int. Conf. on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Gawlikowski, J., et\u00a0al.: A survey of uncertainty in deep neural networks. Artificial Intelligence Review, pp. 1\u201377 (2023)","DOI":"10.1007\/s10462-023-10562-9"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Harakeh, A., Smart, M., Waslander, S.L.: Bayesod: a bayesian approach for uncertainty estimation in deep object detectors. In: 2020 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 87\u201393. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9196544"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"He, Y., Zhu, C., Wang, J., Savvides, M., Zhang, X.: Bounding box regression with uncertainty for accurate object detection. In: Proceedings of the IEEE\/CVF Conf. on Computer Vision and Pattern Recognition, pp. 2888\u20132897 (2019)","DOI":"10.1109\/CVPR.2019.00300"},{"key":"6_CR10","unstructured":"ICMM: Icmm - critical control management: Good practice guide (2015). https:\/\/www.icmm.com\/en-gb\/guidance\/health-safety\/2015\/ccm-good-practice-guide"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"5913","DOI":"10.1007\/s00500-018-3253-3","volume":"23","author":"M Koopialipoor","year":"2019","unstructured":"Koopialipoor, M., Jahed Armaghani, D., Hedayat, A., Marto, A., Gordan, B.: Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft. Comput. 23, 5913\u20135929 (2019)","journal-title":"Soft. Comput."},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Kraus, F., Dietmayer, K.: Uncertainty estimation in one-stage object detection. In: 2019 IEEE Intelligent Transportation Systems Conf. (ITSC), pp. 53\u201360. IEEE (2019)","DOI":"10.1109\/ITSC.2019.8917494"},{"key":"6_CR13","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 (2017)"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Miller, D., Dayoub, F., Milford, M., S\u00fcnderhauf, N.: Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In: 2019 Int. Conf. on Robotics and Automation (ICRA), pp. 2348\u20132354. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793821"},{"issue":"6","key":"6_CR15","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.3390\/s22062315","volume":"22","author":"ME Otgonbold","year":"2022","unstructured":"Otgonbold, M.E., Gochoo, M., Alnajjar, F., Ali, L., Tan, T.H., Hsieh, J.W., Chen, P.Y.: Shel5k: an extended dataset and benchmarking for safety helmet detection. Sensors 22(6), 2315 (2022)","journal-title":"Sensors"},{"issue":"3","key":"6_CR16","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s42154-021-00154-0","volume":"4","author":"L Peng","year":"2021","unstructured":"Peng, L., Wang, H., Li, J.: Uncertainty evaluation of object detection algorithms for autonomous vehicles. Automotive Innov. 4(3), 241\u2013252 (2021)","journal-title":"Automotive Innov."},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Ristovski, K., Gupta, C., Harada, K., Tang, H.K.: Dispatch with confidence: integration of machine learning, optimization and simulation for open pit mines. In: Proceedings of the 23rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 1981\u20131989 (2017)","DOI":"10.1145\/3097983.3098178"},{"issue":"2","key":"6_CR18","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1111\/mice.12579","volume":"36","author":"J Shen","year":"2021","unstructured":"Shen, J., Xiong, X., Li, Y., He, W., Li, P., Zheng, X.: Detecting safety helmet wearing on construction sites with bounding-box regression and deep transfer learning. Comput.-Aided Civil Infrastruct. Eng. 36(2), 180\u2013196 (2021)","journal-title":"Comput.-Aided Civil Infrastruct. Eng."},{"key":"6_CR19","unstructured":"Skalski, P.: Make sense (2019). https:\/\/www.makesense.ai\/"},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.autcon.2018.11.033","volume":"99","author":"H Son","year":"2019","unstructured":"Son, H., Choi, H., Seong, H., Kim, C.: Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks. Autom. Constr. 99, 27\u201338 (2019)","journal-title":"Autom. Constr."},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Scaled-yolov4: scaling cross stage partial network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13029\u201313038 (2021)","DOI":"10.1109\/CVPR46437.2021.01283"},{"issue":"10","key":"6_CR22","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.3390\/s21103478","volume":"21","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Wu, Y., Yang, L., Thirunavukarasu, A., Evison, C., Zhao, Y.: Fast personal protective equipment detection for real construction sites using deep learning approaches. Sensors 21(10), 3478 (2021)","journal-title":"Sensors"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Xiao, B., Kang, S.C.: Development of an image data set of construction machines for deep learning object detection. J. Comput. Civil Eng. 35(2) (2021)","DOI":"10.1061\/(ASCE)CP.1943-5487.0000945"},{"key":"6_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103721","volume":"127","author":"B Xiao","year":"2021","unstructured":"Xiao, B., Lin, Q., Chen, Y.: A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement. Autom. Constr. 127, 103721 (2021)","journal-title":"Autom. Constr."},{"key":"6_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.mineng.2020.106443","volume":"155","author":"M Zarie","year":"2020","unstructured":"Zarie, M., Jahedsaravani, A., Massinaei, M.: Flotation froth image classification using convolutional neural networks. Miner. Eng. 155, 106443 (2020)","journal-title":"Miner. Eng."},{"key":"6_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.123268","volume":"291","author":"T Zeng","year":"2021","unstructured":"Zeng, T., Wang, J., Cui, B., Wang, X., Wang, D., Zhang, Y.: The equipment detection and localization of large-scale construction jobsite by far-field construction surveillance video based on improving yolov3 and grey wolf optimizer improving extreme learning machine. Constr. Build. Mater. 291, 123268 (2021)","journal-title":"Constr. Build. Mater."},{"key":"6_CR27","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018)"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76604-6_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T05:43:13Z","timestamp":1763962993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76604-6_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,17]]},"ISBN":["9783031766039","9783031766046"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76604-6_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,17]]},"assertion":[{"value":"17 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Talca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chile","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ciarp24.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}