棉纺织技术2026，Vol.54Issue(8)：9-16,8.DOI:10.26967/j.issn1000-7415.202508004
基于MBW-Faster RCNN的碳纤维复合材料铣削缺陷检测方法
Milling defect detection method for carbon fiber composite material based on MBW-Faster RCNN
摘要
Abstract
When carbon fiber composites were applied to the metro bogies,defects such as burrs,delamination and voids frequently occurred during the milling process,adversely affecting structural performance and service life.In order to efficiently and accurately recognize processing defects,to address the limitations of the Faster RCNN model—namely its weak edge feature extraction capability and high false detection rate,an enhanced defect detection method MBW-Faster RCNN for detecting milling defects in carbon fiber composite material metro bogies was proposed.Multi-Scale Edge Enhancement(MSEE)module was introduced to improve the model's sensitivity to fine defects and edge details in fiber-textured,cluttered regions by combining multi-scale feature extraction with edge information enhancement.In addition,Bidirectional Feature Pyramid Network(BiFPN)was incorporated to strengthen cross-scale semantic fusion and enable the model to extract richer feature information.WIoU V3 loss function replaced the conventional CIoU as the bounding box regression loss,improving localization accuracy and small target recognition performance.Experimental results demonstrated that the proposed MBW-Faster RCNN mAP@0.5∶0.95 was achieved 94.40%,representing a 9.04 percentage points improvement over the baseline Faster RCNN,and showed significant advantages in key metrics such as precision and loss value.关键词
碳纤维复合材料/地铁转向架/铣削缺陷检测/Faster RCNN/MSEE/BiFPN/Wise-IoUKey words
carbon fiber composite material/metro bogie/milling defect detection/Faster RCNN/MSEE/BiFPN/Wise-IoU分类
信息技术与安全科学引用本文复制引用
何凯龙,李奕辰,甘学辉,平安,周雨桦,江毅文,徐连发..基于MBW-Faster RCNN的碳纤维复合材料铣削缺陷检测方法[J].棉纺织技术,2026,54(8):9-16,8.基金项目
国家重点研发计划项目(2023YFB3709605) （2023YFB3709605）
中央高校基本科研业务费专项资金项目(2232024A-04) （2232024A-04）
