{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:46:48Z","timestamp":1780465608760,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"National Natural Science Foundation of China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"National Natural Science Foundation of China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["52308323"],"award-info":[{"award-number":["52308323"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In recent years, computer vision technology has been extensively applied in the field of defect detection for transportation infrastructure, particularly in the detection of road surface cracks. Given the variations in performance and parameters across different models, this paper proposes an improved Faster R-CNN crack recognition model that incorporates attention mechanisms. The main content of this study includes the use of the residual network ResNet50 as the basic backbone network for feature extraction in Faster R-CNN, integrated with the Squeeze-and-Excitation Network (SENet) to enhance the model\u2019s attention mechanisms. We thoroughly explored the effects of integrating SENet at different layers within each bottleneck of the Faster R-CNN and its specific impact on model performance. Particularly, SENet was added to the third convolutional layer, and its performance enhancement was investigated through 20 iterations. Experimental results demonstrate that the inclusion of SENet in the third convolutional layer significantly improves the model\u2019s accuracy in detecting road surface cracks and optimizes resource utilization after 20 iterations, thereby proving that the addition of SENet substantially enhances the model\u2019s performance.<\/jats:p>","DOI":"10.3390\/sym16081027","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T05:19:38Z","timestamp":1723439978000},"page":"1027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["The Improvement of Faster-RCNN Crack Recognition Model and Parameters Based on Attention Mechanism"],"prefix":"10.3390","volume":"16","author":[{"given":"Qiule","family":"Li","sequence":"first","affiliation":[{"name":"School of Rail Transportation, Soochow University, Suzhou 215131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9713-0535","authenticated-orcid":false,"given":"Xiangyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Rail Transportation, Soochow University, Suzhou 215131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jijie","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Rail Transportation, Soochow University, Suzhou 215131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Sch Transportat & Civil Engn, Nantong University, Nantong 226300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, H., Su, X., Wang, Y., Cai, H., Cui, K., and Chen, X. 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