{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:22:22Z","timestamp":1772821342020,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11042-023-14569-w","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T09:49:30Z","timestamp":1676368170000},"page":"28643-28668","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel target detection and localization method in indoor environment for mobile robot based on improved YOLOv5"],"prefix":"10.1007","volume":"82","author":[{"given":"Weijie","family":"Qian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhua","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanzhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zefeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"14569_CR1","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1007\/s11063-020-10197-9","volume":"51","author":"M Afif","year":"2020","unstructured":"Afif M, Ayachi R, Said Y, Pissaloux E, Atri M (2020) An evaluation of RetinaNet on indoor object detection for blind and visually impaired persons assistance navigation. Neural Process Lett 51:2265\u20132279","journal-title":"Neural Process Lett"},{"key":"14569_CR2","doi-asserted-by":"publisher","first-page":"31645","DOI":"10.1007\/s11042-020-09662-3","volume":"79","author":"M Afif","year":"2020","unstructured":"Afif M, Ayachi R, Pissaloux E, Said Y, Atri M (2020) Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people. Multimed Tools Appl 79:31645\u201331662","journal-title":"Multimed Tools Appl"},{"key":"14569_CR3","doi-asserted-by":"crossref","unstructured":"Amad-ud-Din, Halin IA, Shafie SB (2009) A review on solid state time of flight TOF range image sensors. In: 2009 IEEE Student Conference on Research and Development, pp 246\u2013249","DOI":"10.1109\/SCORED.2009.5443066"},{"key":"14569_CR4","doi-asserted-by":"crossref","unstructured":"Biswas K, Kumar S et al (2021) SMU: smooth activation function for deep networks using smoothing maximum technique. arXiv preprint http:\/\/arXiv.org\/2111.04682","DOI":"10.1109\/CVPR52688.2022.00087"},{"key":"14569_CR5","unstructured":"Bochkovskiy A, Wang CY et al (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint http:\/\/arXiv.org\/2004.10934"},{"key":"14569_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"14569_CR7","volume-title":"Li HJ et al","author":"YX Cai","year":"2020","unstructured":"Cai YX (2020) Li HJ et al. Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design. arXiv preprint, YOLObile http:\/\/arXiv.org\/2009.05697"},{"key":"14569_CR8","doi-asserted-by":"crossref","unstructured":"Chen M, Ren XM et al (2020) Real-time indoor object detection based on deep learning and gradient harmonizing mechanism. In: 2020 IEEE 9th data driven control and learning systems conference, pp 772-777","DOI":"10.1109\/DDCLS49620.2020.9275060"},{"key":"14569_CR9","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE conference on computer vision and pattern recognition, pp 886-893","DOI":"10.1109\/CVPR.2005.177"},{"key":"14569_CR10","doi-asserted-by":"publisher","first-page":"172988142199332","DOI":"10.1177\/1729881421993323","volume":"18","author":"XT Ding","year":"2021","unstructured":"Ding XT, Li BQ, Wang JB (2021) Geometric property-based convolutional neural network for indoor object detection. Int J Adv Robot Syst 18:172988142199332. https:\/\/doi.org\/10.1177\/1729881421993323","journal-title":"Int J Adv Robot Syst"},{"key":"14569_CR11","unstructured":"Feng YX, He GT, Wu QZ (2016) A new motion obstacle detection based monocular-vision algorithm. In: 2016 international conference on computational intelligence and applications, pp 31\u201335"},{"key":"14569_CR12","unstructured":"Ge Z, Liu ST et al (2021) YOLOX: exceeding YOLO series in 2021. arXiv preprint http:\/\/arXiv.org\/2107.08430"},{"key":"14569_CR13","first-page":"315","volume-title":"Deep sparse rectifier neural networks","author":"X Glorot","year":"2011","unstructured":"Glorot X, Bordes A et al (2011) Deep sparse rectifier neural networks. Proceedings of the fourteenth international conference on artificial intelligence and statistics, In, pp 315\u2013323"},{"key":"14569_CR14","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1016\/j.future.2018.05.083","volume":"88","author":"T Hu","year":"2018","unstructured":"Hu T, Zhang H, Zhu XY, Clunis J, Yang G (2018) Depth sensor based human detection for indoor surveillance. Futur Gener Comput Syst 88:540\u2013551","journal-title":"Futur Gener Comput Syst"},{"key":"14569_CR15","doi-asserted-by":"publisher","first-page":"26430","DOI":"10.3390\/s151026430","volume":"15","author":"J Jung","year":"2015","unstructured":"Jung J, Yoon S, Ju S, Heo J (2015) Development of kinematic 3D laser scanning system for indoor mapping and as-built BIM using constrained SLAM. Sensors 15:26430\u201326456","journal-title":"Sensors"},{"key":"14569_CR16","unstructured":"Kim HS, Choi JS (2008) Advanced indoor localization using ultrasonic sensor and digital compass. In: 2008 international conference on control, automation and systems, pp 223-226"},{"key":"14569_CR17","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R, He K, Dollar P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42:318\u2013327","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14569_CR18","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D et al (2016) SSD: single shot multibox detector. In: computer vision \u2013 ECCV 2016, pp 9905:21-37","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"14569_CR19","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91\u2013110","journal-title":"Int J Comput Vis"},{"key":"14569_CR20","doi-asserted-by":"crossref","unstructured":"Lu FX, Peng HT et al (2020) InstanceFusion: real-time instance-level 3D reconstruction using a single RGBD camera. In: 28th Pacific conference on computer graphics and applications, pp 433-445","DOI":"10.1111\/cgf.14157"},{"key":"14569_CR21","first-page":"28","volume-title":"Rectifier nonlinearities improve neural network acoustic models","author":"AL Maas","year":"2013","unstructured":"Maas AL, Hannun AY et al (2013) Rectifier nonlinearities improve neural network acoustic models. In, Proceedings of the thirteenth international conference on machine learning, p 28"},{"key":"14569_CR22","doi-asserted-by":"publisher","unstructured":"Morar A, Moldoveanu A, Mocanu I, Moldoveanu F, Radoi IE, Asavei V, Gradinaru A, Butean A (2020) A comprehensive survey of indoor localization methods based on computer vision. Sensors. 20. https:\/\/doi.org\/10.3390\/s20092641","DOI":"10.3390\/s20092641"},{"key":"14569_CR23","doi-asserted-by":"crossref","unstructured":"Qi CR, Liu W et al (2018) Frustum PointNets for 3D object detection from RGB-D data. In: 31st IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 918-927","DOI":"10.1109\/CVPR.2018.00102"},{"key":"14569_CR24","doi-asserted-by":"publisher","first-page":"1450010","DOI":"10.1142\/S0219843614500108","volume":"11","author":"SY Qu","year":"2014","unstructured":"Qu SY, Meng C (2014) Statistical classification based fast drivable region detection for indoor Mobile robot. Int J HR 11:1450010. https:\/\/doi.org\/10.1142\/S0219843614500108","journal-title":"Int J HR"},{"key":"14569_CR25","first-page":"369","volume-title":"Research on human target recognition algorithm of home service robot based on fast-RCNN","author":"L Quan","year":"2017","unstructured":"Quan L, Pei D, Wang BB et al (2017) Research on human target recognition algorithm of home service robot based on fast-RCNN. International Conference on Intelligent Computation Technology and Automation, In, pp 369\u2013373"},{"key":"14569_CR26","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. arXiv preprint http:\/\/arXiv.org\/1804.02767"},{"key":"14569_CR27","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S et al (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"14569_CR28","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A et al (2017) YOLO9000: better, faster, stronger. In: 2017 IEEE conference on computer vision and pattern recognition, pp 6517\u20136525","DOI":"10.1109\/CVPR.2017.690"},{"key":"14569_CR29","unstructured":"Ren SQ, He KM, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. arXiv preprint http:\/\/arXiv.org\/1506.01497"},{"key":"14569_CR30","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N et al (2019) Generalized intersection over Union: a metric and a loss for bounding box regression. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition, pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"14569_CR31","first-page":"4151","volume":"71","author":"MFS Sabir","year":"2022","unstructured":"Sabir MFS, Mehmood I et al (2022) An automated real-time face mask detection system using transfer learning with faster-rcnn in the era of the covid-19 pandemic. Comput Mater Contin 71:4151\u20134166","journal-title":"Comput Mater Contin"},{"key":"14569_CR32","doi-asserted-by":"crossref","unstructured":"Sun H, Meng ZH et al (2018) A 3D convolutional neural network towards real-time Amodal 3D object detection. In: 25th IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 8331-8338","DOI":"10.1109\/IROS.2018.8593837"},{"key":"14569_CR33","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57:137\u2013154","journal-title":"Int J Comput Vis"},{"key":"14569_CR34","doi-asserted-by":"publisher","unstructured":"Wang S, Sui HG et al (2022) CDSFusion: dense semantic SLAM for indoor environment using CPU computing. Remote Sens 14. https:\/\/doi.org\/10.3390\/rs14040979","DOI":"10.3390\/rs14040979"},{"key":"14569_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"XD Wu","year":"2008","unstructured":"Wu XD, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14:1\u201337","journal-title":"Knowl Inf Syst"},{"key":"14569_CR36","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1111\/mice.12795","volume":"37","author":"JH Xia","year":"2021","unstructured":"Xia JH, Gong J (2021) Precise indoor localization with 3D facility scan data. Comput-Aided Civ Infrastruct Eng 37:1243\u20131259. https:\/\/doi.org\/10.1111\/mice.12795","journal-title":"Comput-Aided Civ Infrastruct Eng"},{"key":"14569_CR37","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1007\/s11263-021-01456-w","volume":"129","author":"Q Xie","year":"2021","unstructured":"Xie Q, Lai YK, Wu J, Wang Z, Zhang Y, Xu K, Wang J (2021) Vote-based 3D object detection with context modeling and SOB-3DNMS. Int J Comput Vis 129:1857\u20131874. https:\/\/doi.org\/10.1007\/s11263-021-01456-w","journal-title":"Int J Comput Vis"},{"key":"14569_CR38","doi-asserted-by":"crossref","unstructured":"Xu YF, Chen J, Yang QN, Guo Q (2019) Human posture recognition and fall detection using Kinect V2 camera. In: 2019 Chinese control conference, pp 8488-8493","DOI":"10.23919\/ChiCC.2019.8865732"},{"key":"14569_CR39","doi-asserted-by":"publisher","unstructured":"Yan B, Fan P, Lei X, Liu Z, Yang F (2021) A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sens 13. https:\/\/doi.org\/10.3390\/rs13091619","DOI":"10.3390\/rs13091619"},{"key":"14569_CR40","first-page":"666","volume-title":"Flexible camera calibration by viewing a plane from unknown orientations","author":"ZY Zhang","year":"1999","unstructured":"Zhang ZY (1999) Flexible camera calibration by viewing a plane from unknown orientations. Proceedings of the seventh international conference on computer vision, In, pp 666\u2013673"},{"key":"14569_CR41","first-page":"880","volume-title":"A novel infrared landmark indoor positioning method based on improved IMM-UKF","author":"Y Zhang","year":"2014","unstructured":"Zhang Y, Chen HS, Luo Y (2014) A Novel Infrared Landmark Indoor Positioning Method Based on Improved IMM-UKF. In: A novel infrared landmark indoor positioning method based on improved IMM-UKF. Applied Mechanics and Materials, In, pp 880\u2013885"},{"key":"14569_CR42","first-page":"12993","volume-title":"Distance-IoU loss: faster and better learning for bounding box regression","author":"ZH Zheng","year":"2020","unstructured":"Zheng ZH, Wang P et al (2020) Distance-IoU loss: faster and better learning for bounding box regression. AAAI Conference on Artificial Intelligence, In, pp 12993\u201313000"},{"key":"14569_CR43","doi-asserted-by":"publisher","unstructured":"Zhou XY, Wang DQ et al (2019) Objects as points. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1904.07850","DOI":"10.48550\/arXiv.1904.07850"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14569-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14569-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14569-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T09:27:31Z","timestamp":1687858051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14569-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,14]]},"references-count":43,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["14569"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14569-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,14]]},"assertion":[{"value":"9 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. It is to specifically state that \u201cNo Competing interests are at stake and there is No Conflict of interest\u201d with other people or organizations that could inappropriately influence or bias the content of the paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}