{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:11:01Z","timestamp":1780449061452,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Projects in Hubei Province","award":["2020BAB138"],"award-info":[{"award-number":["2020BAB138"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Module (CBAM) mechanism layer to the last layer of the backbone network to improve the feature extraction capability. Second, the neck network introduces the Bi-directional Feature Pyramid Network (BiFPN) module to replace the original Path-Aggregation Network (PAN) structure and enhance the multi-scale feature fusion. Finally, we use transfer learning to pre-train the model and improve the generalization ability of the model. The experimental results show that the method achieves an average accuracy of 93.6% mAP and a detection speed of 16.7 FPS for defect detection on the dataset, which can identify metal shaft surface defects quickly and accurately, and is of reference significance for practical industrial applications.<\/jats:p>","DOI":"10.3390\/s23073761","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T01:10:27Z","timestamp":1680743427000},"page":"3761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Defect Detection for Metal Shaft Surfaces Based on an Improved YOLOv5 Algorithm and Transfer Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Bi","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Ministry of Education for Metallurgical Equipment and Control, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanjie","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ministry of Education for Metallurgical Equipment and Control, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"ref_1","unstructured":"Xie, Z. 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