{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T20:16:21Z","timestamp":1778703381794,"version":"3.51.4"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Automating assembly processes in High-Mix, Low Volume (HMLV) manufacturing remains challenging, especially for Small and Medium-sized Enterprises (SMEs). Consequently, many companies still rely on a significant amount of manual operations with an overall low degree of automation. The emergence of artificial intelligence-based algorithms offers potential solutions, enabling assembly automation compatible with multiple products and maintaining overall production flexibility. This paper investigates the application of the YOLO (You Only Look Once) object detection algorithm in an HMLV production line within an SME. The performance of the algorithm was tested for different cases, namely, (a) on different products having similar product features, (b) on completely new products, and (c) under different lighting conditions. The algorithm achieved precision and recall greater than 98<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> and mAP50:95 greater than 97<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>.<\/jats:p>","DOI":"10.1007\/s10845-024-02411-5","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T12:01:41Z","timestamp":1716465701000},"page":"3447-3463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Flexible and robust detection for assembly automation with YOLOv5: a case study on HMLV manufacturing line"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7463-5525","authenticated-orcid":false,"given":"Alexej","family":"Simeth","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-1930","authenticated-orcid":false,"given":"Atal Anil","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3507-1397","authenticated-orcid":false,"given":"Peter","family":"Plapper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"2411_CR1","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.ress.2017.04.016","volume":"167","author":"A Abu-Samah","year":"2017","unstructured":"Abu-Samah, A., Shahzad, M. K., & Zamai, E. (2017). Bayesian based methodology for the extraction and validation of time bound failure signatures for online failure prediction. Reliability Engineering & System Safety, 167, 616\u201362. https:\/\/doi.org\/10.1016\/j.ress.2017.04.016","journal-title":"Reliability Engineering & System Safety"},{"key":"2411_CR2","unstructured":"Adobe. Lizenzfreie Stockfotos und Bilder. Retrieved from https:\/\/stock.adobe.com\/de\/photos"},{"issue":"6","key":"2411_CR3","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1080\/0951192X.2020.1775300","volume":"33","author":"A Alduaij","year":"2020","unstructured":"Alduaij, A., & Hassan, N. M. (2020). Adopting a circular open-field layout in designing flexible manufacturing systems. International Journal of Computer Integrated Manufacturing, 33(6), 572\u2013589. https:\/\/doi.org\/10.1080\/0951192X.2020.1775300","journal-title":"International Journal of Computer Integrated Manufacturing"},{"key":"2411_CR4","unstructured":"Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934."},{"issue":"11\u201312","key":"2411_CR5","doi-asserted-by":"publisher","first-page":"8257","DOI":"10.1007\/s00170-022-08676-5","volume":"119","author":"YW Chen","year":"2022","unstructured":"Chen, Y. W., & Shiu, J. M. (2022). An implementation of YOLO-family algorithms in classifying the product quality for the acrylonitrile butadiene styrene metallization. The International Journal of Advanced Manufacturing Technology, 119(11\u201312), 8257\u2013826. https:\/\/doi.org\/10.1007\/s00170-022-08676-5","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2411_CR6","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., et\u00a0al. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213\u20133223).","DOI":"10.1109\/CVPR.2016.350"},{"key":"2411_CR7","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., et\u00a0al. (2017). Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision (pp. 764\u2013773).","DOI":"10.1109\/ICCV.2017.89"},{"key":"2411_CR8","doi-asserted-by":"crossref","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (Vol.\u00a01, pp. 886\u2013893). IEEE.","DOI":"10.1109\/CVPR.2005.177"},{"issue":"6","key":"2411_CR9","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","volume":"82","author":"T Diwan","year":"2023","unstructured":"Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243\u2013927. https:\/\/doi.org\/10.1007\/s11042-022-13644-y","journal-title":"Multimedia Tools and Applications"},{"key":"2411_CR10","doi-asserted-by":"publisher","first-page":"10211","DOI":"10.1016\/j.rcim.2020.102113","volume":"70","author":"A Downs","year":"2021","unstructured":"Downs, A., Kootbally, Z., Harrison, W., Pilliptchak, P., Antonishek, B., Aksu, M., et al. (2021). Assessing industrial robot agility through international competitions. Robotics and Computer-Integrated Manufacturing, 70, 10211. https:\/\/doi.org\/10.1016\/j.rcim.2020.102113","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"2411_CR11","volume-title":"Deep learning for vision systems","author":"M Elgendy","year":"2020","unstructured":"Elgendy, M. (2020). Deep learning for vision systems. Simon and Schuster."},{"key":"2411_CR12","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111, 98\u2013136. https:\/\/doi.org\/10.1007\/s11263-014-0733-5","journal-title":"International Journal of Computer Vision"},{"key":"2411_CR13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88, 303\u201333. https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"International Journal of Computer Vision"},{"key":"2411_CR14","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In 2008 IEEE conference on computer vision and pattern recognition (pp. 1\u20138). IEEE.","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"2411_CR15","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P. F., Girshick, R. B., & McAllester, D. (2010). Cascade object detection with deformable part models. In 2010 IEEE computer society conference on computer vision and pattern recognition (pp. 2241\u20132248). IEEE.","DOI":"10.1109\/CVPR.2010.5539906"},{"issue":"3","key":"2411_CR16","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.jmsy.2012.02.001","volume":"31","author":"R Fernandes","year":"2012","unstructured":"Fernandes, R., Gouveia, J. B., & Pinho, C. (2012). Product mix strategy and manufacturing flexibility. Journal of Manufacturing Systems, 31(3), 301\u201331. https:\/\/doi.org\/10.1016\/j.jmsy.2012.02.001","journal-title":"Journal of Manufacturing Systems"},{"issue":"1","key":"2411_CR17","doi-asserted-by":"publisher","first-page":"e651","DOI":"10.1002\/cpe.6517","volume":"34","author":"ML Francies","year":"2022","unstructured":"Francies, M. L., Ata, M. M., & Mohamed, M. A. (2022). A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms. Concurrency and Computation: Practice and Experience, 34(1), e651. https:\/\/doi.org\/10.1002\/cpe.6517","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"2411_CR18","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440\u20131448).","DOI":"10.1109\/ICCV.2015.169"},{"key":"2411_CR19","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580\u2013587).","DOI":"10.1109\/CVPR.2014.81"},{"issue":"1","key":"2411_CR20","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","volume":"38","author":"R Girshick","year":"2015","unstructured":"Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2015). Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142\u2013158. https:\/\/doi.org\/10.1109\/TPAMI.2015.2437384","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2411_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961\u20132969).","DOI":"10.1109\/ICCV.2017.322"},{"key":"2411_CR22","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.procir.2016.04.129","volume":"50","author":"P Holtewert","year":"2016","unstructured":"Holtewert, P., & Bauernhansl, T. (2016). Interchangeable product designs for the increase of capacity flexibility in production systems. Procedia CIRP, 50, 252\u2013257. https:\/\/doi.org\/10.1016\/j.procir.2016.04.129","journal-title":"Procedia CIRP"},{"key":"2411_CR23","doi-asserted-by":"publisher","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","volume":"7","author":"L Jiao","year":"2019","unstructured":"Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., et al. (2019). A survey of deep learning-based object detection. IEEE Access, 7, 128837\u201312886. https:\/\/doi.org\/10.1109\/ACCESS.2019.2939201","journal-title":"IEEE Access"},{"issue":"2","key":"2411_CR24","first-page":"271","volume":"46","author":"LT Jiao","year":"2022","unstructured":"Jiao, L. T., Guo, P. W., Hong, B., & Feng, P. (2022). Vehicle wheel weld detection based on improved YOLO v4 algorithm. Computer Optics, 46(2), 271\u2013279.","journal-title":"Computer Optics"},{"key":"2411_CR25","unstructured":"Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., et\u00a0al. (2022) ultralytics\/yolov5: v7.0\u2014YOLOv5 SOTA realtime instance segmentation. Retrieved from https:\/\/zenodo.org\/record\/7347926"},{"key":"2411_CR26","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1016\/j.procir.2021.11.245","volume":"104","author":"K Johansen","year":"2021","unstructured":"Johansen, K., Rao, S., & Ashourpour, M. (2021). The role of automation in complexities of high-mix in low-volume production-a literature review. Procedia CIRP, 104, 1452\u20131457. https:\/\/doi.org\/10.1016\/j.procir.2021.11.245","journal-title":"Procedia CIRP"},{"key":"2411_CR27","doi-asserted-by":"crossref","unstructured":"Karaulova, T., Andronnikov, K., Mahmood, K., & Shevtshenko, E. (2019). Lean automation for low-volume manufacturing environment. In B. Katalinic (Ed.), Proceedings of the 30th DAAAM international symposium (pp. 0059\u20130068). DAAAM International.","DOI":"10.2507\/30th.daaam.proceedings.008"},{"issue":"27","key":"2411_CR28","doi-asserted-by":"publisher","first-page":"38297","DOI":"10.1007\/s11042-022-13153-y","volume":"81","author":"J Kaur","year":"2022","unstructured":"Kaur, J., & Singh, W. (2022). Tools, techniques, datasets and application areas for object detection in an image: A review. Multimedia Tools and Applications, 81(27), 38297\u20133835. https:\/\/doi.org\/10.1007\/s11042-022-13153-y","journal-title":"Multimedia Tools and Applications"},{"key":"2411_CR29","first-page":"41","volume":"3","author":"M Kleindienst","year":"2015","unstructured":"Kleindienst, M., & Ramsauer, C. (2015). Der Beitrag von Lernfabriken zu Industrie 4.0-Ein Baustein zur vierten industriellen Revolution bei kleinen und mittelst\u00e4ndischen Unternehmen. Industrie-Management, 3, 41\u201344.","journal-title":"Industrie-Management"},{"issue":"18","key":"2411_CR30","doi-asserted-by":"publisher","first-page":"375","DOI":"10.3390\/app9183750","volume":"9","author":"J Li","year":"2019","unstructured":"Li, J., Gu, J., Huang, Z., & Wen, J. (2019). Application research of improved YOLO V3 algorithm in PCB electronic component detection. Applied Sciences, 9(18), 375. https:\/\/doi.org\/10.3390\/app9183750","journal-title":"Applied Sciences"},{"key":"2411_CR31","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980\u20132988).","DOI":"10.1109\/ICCV.2017.324"},{"key":"2411_CR32","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P, Ramanan, D., et\u00a0al. (2014). Microsoft coco: Common objects in context. In Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13 (pp. 740\u2013755). Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2411_CR33","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., et\u00a0al. (2016). Ssd: Single shot multibox detector. In Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, Proceedings, Part I 14 (pp. 21\u201337). Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2411_CR34","doi-asserted-by":"crossref","unstructured":"Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision (Vol.\u00a02, pp. 1150\u20131157). IEEE.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"2411_CR35","doi-asserted-by":"crossref","unstructured":"Malisiewicz, T., Gupta, A., & Efros, A. A. (2011). Ensemble of exemplar-svms for object detection and beyond. In 2011 international conference on computer vision (pp. 89\u201396). IEEE.","DOI":"10.1109\/ICCV.2011.6126229"},{"key":"2411_CR36","doi-asserted-by":"crossref","unstructured":"Mo, Z., Chen, L., & You, W. (2019). Identification and detection of automotive door panel solder joints based on YOLO. In Chinese control and decision conference (CCDC) (pp. 5956\u20135960). IEEE.","DOI":"10.1109\/CCDC.2019.8833257"},{"issue":"13","key":"2411_CR37","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ifacol.2019.11.136","volume":"52","author":"R M\u00fcller","year":"2019","unstructured":"M\u00fcller, R., Vette-Steinkamp, M., & Kanso, A. (2019). Position and orientation calibration of a 2D laser line sensor using closed-form least-squares solution. IFAC-PapersOnLine, 52(13), 689\u2013694. https:\/\/doi.org\/10.1016\/j.ifacol.2019.11.136","journal-title":"IFAC-PapersOnLine"},{"issue":"23","key":"2411_CR38","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.3390\/app112311229","volume":"11","author":"SS Park","year":"2021","unstructured":"Park, S. S., Tran, V. T., & Lee, D. E. (2021). Application of various YOLO models for computer vision-based real-time pothole detection. Applied Sciences, 11(23), 1122. https:\/\/doi.org\/10.3390\/app112311229","journal-title":"Applied Sciences"},{"issue":"6","key":"2411_CR39","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/jimaging6060048","volume":"6","author":"P Pierleoni","year":"2020","unstructured":"Pierleoni, P., Belli, A., Palma, L., & Sabbatini, L. (2020). A versatile machine vision algorithm for real-time counting manually assembled pieces. Journal of Imaging, 6(6), 4. https:\/\/doi.org\/10.3390\/jimaging6060048","journal-title":"Journal of Imaging"},{"key":"2411_CR40","unstructured":"Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767."},{"key":"2411_CR41","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779\u2013788).","DOI":"10.1109\/CVPR.2016.91"},{"key":"2411_CR42","doi-asserted-by":"crossref","unstructured":"Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263\u20137271).","DOI":"10.1109\/CVPR.2017.690"},{"key":"2411_CR43","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28."},{"issue":"2","key":"2411_CR44","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s40684-021-00343-6","volume":"9","author":"Z Ren","year":"2022","unstructured":"Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661\u201369. https:\/\/doi.org\/10.1007\/s40684-021-00343-6","journal-title":"International Journal of Precision Engineering and Manufacturing-Green Technology"},{"key":"2411_CR45","doi-asserted-by":"crossref","unstructured":"Tahmina, T., Garcia, M., Geng, Z., & Bidanda, B. (2022). A survey of smart manufacturing for high-mix low-volume production in defense and aerospace industries. In: International conference on flexible automation and intelligent manufacturing (p. 237\u2013245). Springer.","DOI":"10.1007\/978-3-031-18326-3_24"},{"issue":"4","key":"2411_CR46","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven, J., C\u00f3rdova-Esparza, D. M., & Romero-Gonz\u00e1lez, J. A. (2023). A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680\u20131716. https:\/\/doi.org\/10.3390\/make5040083","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"2411_CR47","unstructured":"Tkachenko, M., Malyuk, M., Holmanyuk, A., & Liubimov, N. (2020) Label studio: Data labeling software. Retrieved from https:\/\/github.com\/heartexlabs\/label-studio"},{"key":"2411_CR48","doi-asserted-by":"crossref","unstructured":"Transeth, A. A., Stepanov, A., Linnerud, \u00c5. S., Ening, K., & Gjerstad, T. (2020). Competitive high variance, low volume manufacturing with robot manipulators. In 3rd international symposium on small-scale intelligent manufacturing systems (SIMS) (pp. 1\u20137). IEEE.","DOI":"10.1109\/SIMS49386.2020.9121464"},{"key":"2411_CR49","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.promfg.2018.02.034","volume":"20","author":"S Vaidya","year":"2018","unstructured":"Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0\u2013a glimpse. Procedia Manufacturing, 20, 233\u2013238. https:\/\/doi.org\/10.1016\/j.promfg.2018.02.034","journal-title":"Procedia Manufacturing"},{"key":"2411_CR50","doi-asserted-by":"crossref","unstructured":"Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition (Vol.\u00a01, pp. I\u2013I). IEEE.","DOI":"10.1109\/CVPR.2001.990517"},{"key":"2411_CR51","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, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57, 137\u2013154. https:\/\/doi.org\/10.1023\/B:VISI.0000013087.49260.fb","journal-title":"International Journal of Computer Vision"},{"key":"2411_CR52","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.procir.2021.11.173","volume":"104","author":"L Yi","year":"2021","unstructured":"Yi, L., Siedler, C., Kinkel, Y., Glatt, M., K\u00f6lsch, P., & Aurich, J. C. (2021). Object detection in factory based on deep learning approach. Procedia CIRP, 104, 1029\u2013103. https:\/\/doi.org\/10.1016\/j.procir.2021.11.173","journal-title":"Procedia CIRP"},{"key":"2411_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018). Single-shot refinement neural network for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4203\u20134212).","DOI":"10.1109\/CVPR.2018.00442"},{"issue":"11","key":"2411_CR54","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/34.888718","volume":"22","author":"Z Zhang","year":"2000","unstructured":"Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330\u20131334. https:\/\/doi.org\/10.1109\/34.888718","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2411_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Sheng, T., Wang, Y., Tang, Z., Chen, Y., Cai, L., et\u00a0al. (2019). M2det: A single-shot object detector based on multi-level feature pyramid network. In Proceedings of the AAAI conference on artificial intelligence (Vol.\u00a033, pp. 9259\u20139266).","DOI":"10.1609\/aaai.v33i01.33019259"},{"key":"2411_CR56","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1007\/s10489-020-01877-z","volume":"51","author":"X Zheng","year":"2021","unstructured":"Zheng, X., Chen, J., Wang, H., Zheng, S., & Kong, Y. (2021). A deep learning-based approach for the automated surface inspection of copper clad laminate images. Applied Intelligence, 51, 1262\u20131279. https:\/\/doi.org\/10.1007\/s10489-020-01877-z","journal-title":"Applied Intelligence"},{"key":"2411_CR57","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2019) Distance-IoU loss: Faster and better learning for bounding box regression. Retrieved from http:\/\/arxiv.org\/abs\/1911.08287"},{"issue":"5","key":"2411_CR58","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/J.ENG.2017.05.015","volume":"3","author":"RY Zhong","year":"2017","unstructured":"Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616\u201363. https:\/\/doi.org\/10.1016\/J.ENG.2017.05.015","journal-title":"Engineering"},{"key":"2411_CR59","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017). Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 633\u2013641).","DOI":"10.1109\/CVPR.2017.544"},{"issue":"3","key":"2411_CR60","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3), 257\u2013276. https:\/\/doi.org\/10.1109\/JPROC.2023.3238524","journal-title":"Proceedings of the IEEE"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02411-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02411-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02411-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T20:50:46Z","timestamp":1747687846000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02411-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":60,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2411"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02411-5","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,23]]},"assertion":[{"value":"25 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}