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Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings.<\/jats:p>","DOI":"10.3390\/s24103002","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T10:31:16Z","timestamp":1715250676000},"page":"3002","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on Bearing Surface Scratch Detection Based on Improved YOLOV5"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0909-5507","authenticated-orcid":false,"given":"Huakun","family":"Jia","sequence":"first","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Huimin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Zhehao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Rongke","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Liandong","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial Intelligence for Fault Diagnosis of Rotating Machinery: A Review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lv, X., Duan, F., Jiang, J., Fu, X., and Gan, L. (2020). Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network. Sensors, 20.","DOI":"10.3390\/s20061562"},{"key":"ref_3","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P.F., McAllester, D.A., and Ramanan, D. (2008, January 23\u201328). A Discriminatively Trained, Multiscale, Deformable Part Model. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object Detection with Discriminatively Trained Part Based Models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P.F., Girshick, R.B., and McAllester, D.A. (2010, January 13\u201318). Cascade Object Detection with Deformable Part Models. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539906"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_10","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_14","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_15","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_17","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20939","DOI":"10.1007\/s00521-023-08809-1","article-title":"An Improved Fire Detection Approach Based on YOLO-v8 for Smart Cities","volume":"35","author":"Talaat","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gu, Z., Liu, X., and Wei, L. (2021, January 8\u201310). A Detection and Identification Method Based on Machine Vision for Bearing Surface Defects. Proceedings of the 2021 International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China.","DOI":"10.1109\/ICCCR49711.2021.9349370"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2300329","DOI":"10.1299\/jamdsm.2023jamdsm0071","article-title":"Defect Detection of Bearing Side Face Based on Sample Data Augmentation and Convolutional Neural Network","volume":"17","author":"Liang","year":"2023","journal-title":"J. Adv. Mech. Des. Syst. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31299","DOI":"10.1016\/j.ceramint.2022.06.205","article-title":"A Nondestructive Testing Method for Detecting Surface Defects of Si3N4-Bearing Cylindrical Rollers Based on an Optimized Convolutional Neural Network","volume":"48","author":"Liao","year":"2022","journal-title":"Ceram. Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"164517","DOI":"10.1016\/j.ijleo.2020.164517","article-title":"An Automatic System for Bearing Surface Tiny Defect Detection Based on Multi-Angle Illuminations","volume":"208","author":"Liu","year":"2020","journal-title":"Optik"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10304","DOI":"10.1109\/ACCESS.2021.3050484","article-title":"Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3","volume":"9","author":"Zheng","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., and Hsieh, J.-W. (2020, January 14\u201319). CSPNet: A New Backbone That Can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_29","unstructured":"Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable ConvNets V2: More Deformable, Better Results. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). GhostNet: More Features from Cheap Operations. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3002\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:42:40Z","timestamp":1760107360000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24103002"],"URL":"https:\/\/doi.org\/10.3390\/s24103002","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,9]]}}}