{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:38:28Z","timestamp":1783384708281,"version":"3.54.6"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T00:00:00Z","timestamp":1693008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D Program of Zhejiang","award":["2023C01062"],"award-info":[{"award-number":["2023C01062"]}]},{"name":"Key R&amp;D Program of Zhejiang","award":["LGF22F030001"],"award-info":[{"award-number":["LGF22F030001"]}]},{"name":"Key R&amp;D Program of Zhejiang","award":["LGG19F03001"],"award-info":[{"award-number":["LGG19F03001"]}]},{"name":"Basic Public Welfare Research Program of Zhejiang Province","award":["2023C01062"],"award-info":[{"award-number":["2023C01062"]}]},{"name":"Basic Public Welfare Research Program of Zhejiang Province","award":["LGF22F030001"],"award-info":[{"award-number":["LGF22F030001"]}]},{"name":"Basic Public Welfare Research Program of Zhejiang Province","award":["LGG19F03001"],"award-info":[{"award-number":["LGG19F03001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Considering the characteristics of complex texture backgrounds, uneven brightness, varying defect sizes, and multiple defect types of the bearing surface images, a surface defect detection method for bearing rings is proposed based on improved YOLOv5. First, replacing the C3 module in the backbone network with a C2f module can effectively reduce the number of network parameters and computational complexity, thereby improving the speed and accuracy of the backbone network. Second, adding the SPD module into the backbone and neck networks enhances their ability to process low-resolution and small-object images. Next, replacing the nearest-neighbor upsampling with the lightweight and universal CARAFE operator fully utilizes feature semantic information, enriches contextual information, and reduces information loss during transmission, thereby effectively improving the model\u2019s diversity and robustness. Finally, we constructed a dataset of bearing ring surface images collected from industrial sites and conducted numerous experiments based on this dataset. Experimental results show that the mean average precision (mAP) of the network is 97.3%, especially for dents and black spot defects, improved by 2.2% and 3.9%, respectively, and that the detection speed can reach 100 frames per second (FPS). Compared with mainstream surface defect detection algorithms, the proposed method shows significant improvements in both accuracy and detection time and can meet the requirements of industrial defect detection.<\/jats:p>","DOI":"10.3390\/s23177443","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T06:10:22Z","timestamp":1693203022000},"page":"7443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Surface Defect Detection of Bearing Rings Based on an Improved YOLOv5 Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Haitao","family":"Xu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Changshan Research Institute, Zhejiang Sci-Tech University, Quzhou 324299, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8736-0716","authenticated-orcid":false,"given":"Haipeng","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Changshan Research Institute, Zhejiang Sci-Tech University, Quzhou 324299, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-3317","authenticated-orcid":false,"given":"Junfeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Changshan Research Institute, Zhejiang Sci-Tech University, Quzhou 324299, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.renene.2022.08.054","article-title":"Evaluation of opaque deep-learning solar power forecast models towards power-grid applications","volume":"198","author":"Cheng","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/TIA.2022.3206731","article-title":"Dynamic Feature Selection for Solar Irradiance Forecasting Based on Deep Reinforcement Learning","volume":"59","author":"Lyu","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_3","first-page":"1843","article-title":"Analysis of Recent Deep-Learning-Based Intrusion Detection Methods for In-Vehicle Network","volume":"24","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TVT.2022.3205452","article-title":"Deep reinforcement learning based active pantograph control strategy in high-speed railway","volume":"72","author":"Wang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e09317","DOI":"10.1016\/j.heliyon.2022.e09317","article-title":"A review on machine learning and deep learning for various antenna design applications","volume":"8","author":"Khan","year":"2022","journal-title":"Heliyon"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2200252","DOI":"10.1002\/adma.202200252","article-title":"A Deep-Learning-Assisted On-Mask Sensor Network for Adaptive Respiratory Monitoring","volume":"34","author":"Fang","year":"2022","journal-title":"Adv. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/TMI.2022.3213983","article-title":"Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning","volume":"42","author":"Hering","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_8","first-page":"5083","article-title":"A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions","volume":"34","author":"Pandey","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Singh, S.P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., and Guly\u00e1s, B. (2020). 3D deep learning on medical images: A review. Sensors, 20.","DOI":"10.3390\/s20185097"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117531","DOI":"10.1016\/j.jmatprotec.2022.117531","article-title":"Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process","volume":"303","author":"Pandiyan","year":"2022","journal-title":"J. Mater. Process. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.jmrt.2022.01.172","article-title":"Phase formation prediction of high-entropy alloys: A deep learning study","volume":"18","author":"Zhu","year":"2022","journal-title":"J. Mater. Res. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1080\/0951192X.2021.1992660","article-title":"An optical system for identifying and classifying defects of metal parts","volume":"35","author":"Papavasileiou","year":"2022","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_13","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":"Opt. Int. J. Light Electron Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24323","DOI":"10.1109\/ACCESS.2020.2970813","article-title":"A novel rolling bearing defect detection method based on bispectrum analysis and cloud model-improved EEMD","volume":"8","author":"Jiang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.isatra.2015.10.014","article-title":"Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time\u2013frequency ridge enhancement","volume":"60","author":"Li","year":"2016","journal-title":"ISA Trans."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.apacoust.2014.04.005","article-title":"A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects","volume":"85","author":"Wang","year":"2014","journal-title":"Appl. Acoust."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","article-title":"Segmentation-based deep-learning approach for surface-defect detection","volume":"31","author":"Tabernik","year":"2019","journal-title":"J. Intell. Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9544809","DOI":"10.1155\/2021\/9544809","article-title":"Bearing Defect Detection with Unsupervised Neural Networks","volume":"2021","author":"Xu","year":"2021","journal-title":"Shock. Vib."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lei, L., Sun, S., Zhang, Y., Liu, H., and Xie, H. (2021). Segmented embedded rapid defect detection method for bearing surface defects. Machines, 9.","DOI":"10.3390\/machines9020040"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ifacol.2018.09.412","article-title":"Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network","volume":"51","author":"Li","year":"2018","journal-title":"IFAC-Pap. Online"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2019.11.002","article-title":"A two-stage attention aware method for train bearing shed oil inspection based on convolutional neural networks","volume":"380","author":"Fu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1016\/j.aej.2020.03.034","article-title":"Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)","volume":"59","author":"Kumar","year":"2020","journal-title":"Alex. Eng. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Song, K.K., Zhao, M., Liao, X., Tian, X., Zhu, Y., Xiao, J., and Peng, C. (2022, January 18\u201320). An Improved Bearing Defect Detection Algorithm Based on Yolo. Proceedings of the 2022 International Symposium on Control Engineering and Robotics (ISCER), Changsha, China.","DOI":"10.1109\/ISCER55570.2022.00038"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1109\/ACCESS.2023.3233964","article-title":"YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images","volume":"11","author":"Liu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_25","unstructured":"Sunkara, R., and Luo, T. (2022). Machine Learning and Knowledge Discovery in Databases, Springer."},{"key":"ref_26","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023, June 03). YOLO by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dumitrescu, D., and Boiangiu, C.A. (2019). A study of image upsampling and downsampling filters. Computers, 8.","DOI":"10.3390\/computers8020030"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., and Lin, D. (2019). Carafe: Content-aware reassembly of features. arXiv.","DOI":"10.1109\/ICCV.2019.00310"},{"key":"ref_29","unstructured":"Zhao, H., Gallo, O., Frosio, I., and Kautz, J. (2015). Loss functions for neural networks for image processing. arXiv."},{"key":"ref_30","first-page":"12993","article-title":"Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI conference on artificial intelligence","volume":"34","author":"Zheng","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_31","unstructured":"Chen, P., Liu, S., Zhao, H., and Jia, J. (2020). Gridmask data augmentation. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., and Le, Q.V. (2020, January 14\u201319). Randaugment: Practical automated data augmentation with a reduced search space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dadboud, F., Patel, V., Mehta, V., Bolic, M., and Mantegh, I. (2021, January 16\u201319). Single-stage uav detection and classification with yolov5: Mosaic data augmentation and panet. Proceedings of the 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Washington, DC, USA.","DOI":"10.1109\/AVSS52988.2021.9663841"},{"key":"ref_34","first-page":"304","article-title":"Confusion Matrix: Machine Learning","volume":"3","author":"Liang","year":"2022","journal-title":"POGIL Act. Clgh."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:39:47Z","timestamp":1760128787000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,26]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177443"],"URL":"https:\/\/doi.org\/10.3390\/s23177443","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,26]]}}}