{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T21:40:55Z","timestamp":1722721255507},"reference-count":41,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Copper elbows are an important product in industry. They are used to connect pipes for transferring gas, oil, and liquids. Defective copper elbows can lead to serious industrial accidents. In this paper, a novel model named YOT-Net (YOLOv3 combined triplet loss network) is proposed to automatically detect defective copper elbows. To increase the defect detection accuracy, triplet loss function is employed in YOT-Net. The triplet loss function is introduced into the loss module of YOT-Net, which utilizes image similarity to enhance feature extraction ability. The proposed method of YOT-Net shows outstanding performance in copper elbow surface defect detection.<\/jats:p>","DOI":"10.3390\/s21217260","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T02:24:22Z","timestamp":1635819862000},"page":"7260","source":"Crossref","is-referenced-by-count":6,"title":["YOT-Net: YOLOv3 Combined Triplet Loss Network for Copper Elbow Surface Defect Detection"],"prefix":"10.3390","volume":"21","author":[{"given":"Yuanqing","family":"Xian","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"},{"name":"School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China"}]},{"given":"Guangjun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Tongji University, Shanghai 201804, China"}]},{"given":"Jinfu","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}]},{"given":"Zhongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.patcog.2004.07.009","article-title":"Wavelet based methods on patterned fabric defect detection","volume":"38","author":"Ngan","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_2","unstructured":"Deutschl, E., Gasser, C., Niel, A., and Werschonig, J. (2004, January 1\u20132). Defect detection on rail surfaces by a vision based system. Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1117\/1.1517290","article-title":"Discriminative fabric defect detection using adaptive wavelets","volume":"41","author":"Yang","year":"2002","journal-title":"Opt. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TIM.2019.2963555","article-title":"Automated visual defect detection for flat steel surface: A survey","volume":"69","author":"Luo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Aiger, D., and Talbot, H. (2012). The phase only transform for unsupervised surface defect detection. Emerging Topics in Computer Vision and Its Applications, World Scientific.","DOI":"10.1142\/9789814343008_0011"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1364\/AO.50.005122","article-title":"Pinhole detection in steel slab images using Gabor filter and morphological features","volume":"50","author":"Choi","year":"2011","journal-title":"Appl. Opt."},{"key":"ref_7","first-page":"224","article-title":"Automated surface defect detection for cold-rolled steel strip based on wavelet anisotropic diffusion method","volume":"17","author":"Liu","year":"2014","journal-title":"Int. J. Ind. Syst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/TCYB.2014.2326059","article-title":"Texture classification and retrieval using shearlets and linear regression","volume":"45","author":"Dong","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_9","unstructured":"Goumeidane, A.B., Khamadja, M., and Naceredine, N. (October, January 28). Bayesian pressure snake for weld defect detection. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Bordeaux, France."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, F., Valiente, J.M., and Prats, J.M. (2005, January 15\u201318). Surface grading using soft colour-texture descriptors. Proceedings of the Iberoamerican Congress on Pattern Recognition, Havana, Cuba.","DOI":"10.1007\/11578079_2"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yin, Y., Zhang, K., and Lu, W. (2009, January 26\u201329). Textile flaw classification by wavelet reconstruction and BP neural network. Proceedings of the International Symposium on Neural Networks, Wuhan, China.","DOI":"10.1109\/GCIS.2009.284"},{"key":"ref_12","unstructured":"Jia, H., Murphey, Y.L., Shi, J., and Chang, T.S. (2004, January 26). An intelligent real-time vision system for surface defect detection. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10044-004-0232-3","article-title":"Detection of surface defects on raw steel blocks using Bayesian network classifiers","volume":"7","author":"Pernkopf","year":"2004","journal-title":"Pattern Anal. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s00138-004-0148-3","article-title":"Filter-based feature selection for rail defect detection","volume":"15","author":"Mandriota","year":"2004","journal-title":"Mach. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TIM.2012.2218677","article-title":"Automatic defect detection on hot-rolled flat steel products","volume":"62","author":"Ghorai","year":"2012","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_21","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","article-title":"An end-to-end steel surface defect detection approach via fusing multiple hierarchical features","volume":"69","author":"He","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, H., Xie, Q., Yao, L., and Chen, Q. (2018). Research on a surface defect detection algorithm based on MobileNet-SSD. Appl. Sci., 8.","DOI":"10.3390\/app8091678"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/mice.12500","article-title":"Concrete bridge surface damage detection using a single-stage detector","volume":"35","author":"Zhang","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1080\/03019233.2020.1816806","article-title":"Surface defect detection of steel strips based on classification priority YOLOv3-dense network","volume":"48","author":"Zhang","year":"2020","journal-title":"Ironmak. Steelmak."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"70130","DOI":"10.1109\/ACCESS.2019.2913620","article-title":"Fabric defect detection using activation layer embedded convolutional neural network","volume":"7","author":"Ouyang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.asoc.2016.10.030","article-title":"Automatic surface defect detection for mobile phone screen glass based on machine vision","volume":"52","author":"Jian","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"89278","DOI":"10.1109\/ACCESS.2019.2925561","article-title":"Real-time tiny part defect detection system in manufacturing using deep learning","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"61973","DOI":"10.1109\/ACCESS.2020.2984264","article-title":"Multi-target defect identification for railway track line based on image processing and improved YOLOv3 model","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TIM.2017.2775345","article-title":"Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network","volume":"67","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_37","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_39","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_41","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T01:17:35Z","timestamp":1721524655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,31]]},"references-count":41,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217260"],"URL":"https:\/\/doi.org\/10.3390\/s21217260","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,31]]}}}