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Traditional damage detection is mainly done manually, which is time-consuming and error-prone. In recent years, computer vision methods have been introduced into the community of surface damage detection. However, some advanced typical object detection methods perform poorly in the detection of surface damage on RSS due to the complex surface background and rich diversity of damage patterns and scales. To address these issues, we propose a Faster R-CNN\u2013based surface damage detection method for RSS. To improve the adaptability of the network, we endow it with a feature pyramid network (FPN) as well as adaptable multiscale information modifications to the region proposal network (RPN). In this paper, a detailed study of an FPN-based feature extraction network and the multiscale object detection network is conducted. Experimental results show that our method improves the mean average precision (mAP) score by 8.9% compared with the original Faster R-CNN for surface damage detection of RSS, and the average detection accuracy for small objects is improved by 18.2%. Compared with the current advanced object detection methods, our method is more advantageous for the detection of multiscale objects.<\/jats:p>","DOI":"10.1115\/1.4064257","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T17:21:59Z","timestamp":1702488119000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["Multiscale Feature Fusion Convolutional Neural Network for Surface Damage Detection in Retired Steel Shafts"],"prefix":"10.1115","volume":"24","author":[{"given":"Weiwei","family":"Liu","sequence":"first","affiliation":[{"name":"Dalian University of Technology School of Mechanical Engineering;, State Key Laboratory of High-Performance Precision Manufacturing, , Dalian 116024, Liaoning , China ; , Ningbo 315000, Zhejiang , China"},{"name":"Ningbo Institute of Dalian University of Technology School of Mechanical Engineering;, State Key Laboratory of High-Performance Precision Manufacturing, , Dalian 116024, Liaoning , China ; , Ningbo 315000, Zhejiang , China"}]},{"given":"Jiahe","family":"Qiu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology School of Mechanical Engineering, , Dalian 116024, Liaoning , China"}]},{"given":"Yujiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Army Academy of Armored Forces National Key Laboratory for Remanufacturing, , Beijing 100072 , China"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"Dalian University of Technology School of Mechanical Engineering;, State Key Laboratory of High-Performance Precision Manufacturing, , Dalian 116024, Liaoning , China ; , Ningbo 315000, Zhejiang , China"},{"name":"Ningbo Institute of Dalian University of Technology School of Mechanical Engineering;, State Key Laboratory of High-Performance Precision Manufacturing, , Dalian 116024, Liaoning , China ; , Ningbo 315000, Zhejiang , China"}]},{"given":"Shujie","family":"Liu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology School of Mechanical Engineering;, State Key Laboratory of High-Performance Precision Manufacturing, , Dalian 116024, Liaoning , China ; , Ningbo 315000, Zhejiang , China"},{"name":"Ningbo Institute of Dalian University of Technology School of Mechanical Engineering;, State Key Laboratory of High-Performance Precision Manufacturing, , Dalian 116024, Liaoning , China ; , Ningbo 315000, Zhejiang , China"}]},{"given":"Guangda","family":"Hu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology School of Mechanical Engineering, , Dalian 116024, Liaoning , China"}]},{"given":"Lin","family":"Xue","sequence":"additional","affiliation":[{"name":"Dalian University of Technology School of Mechanical Engineering, , Dalian 116024, Liaoning , China"}]}],"member":"33","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"issue":"4","key":"2024010814304793900_CIT0001","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1177\/0583102404045439","article-title":"Cracked Shaft Detection and Diagnostics: A Literature Review","volume":"36","author":"Sabnavis","year":"2004","journal-title":"Shock Vibr. 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