文献类型：期刊文献

英文题名：YOLO-CDP: An Improved Algorithm for Bearing Surface Defect Detection

作者：Yao, Jingli[1]; Cheng, Guang[2]; Fang, Tianrui[3]; Cao, Zhiyong[3]

第一作者：Yao, Jingli

机构：[1] Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, 100101, China; [2] Frontier Intelligent Technology Research Institute, Beijing Union University, Beijing, 100101, China; [3] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[2]Frontier Intelligent Technology Research Institute, Beijing Union University, Beijing, 100101, China|[11417]北京联合大学;

年份：2024

外文期刊名：2024 2nd International Conference on Computer, Vision and Intelligent Technology, ICCVIT 2024 - Proceedings

收录：EI(收录号：20251018004402)

语种：英文

外文关键词：Object recognition

摘要：To address the challenges of low detection accuracy for tiny defects and the issue of large model sizes in bearing surface defect detection, we present YOLO-CDP, a lightweight detection algorithm developed on the foundation of YOLOv8n. First, we design the C2f-DSConv module to enable the model to sense defects of different scales and shapes, and improve the accuracy of defect identification and localization. Second, we use the feature-based content-aware reassembly upsampling operator CARAFE in the neck, helping the model capture richer contextual information and enhancing its feature representation capacity. Finally, we add a new small object detection layer for detecting small-size defects on the bearing surface. Experimental results indicate that YOLO-CDP achieves a mAP of 92.4%, an increase of 3.8% over the original algorithm. The model has only 3.4M parameters, ensuring lightweight efficiency while enhancing detection accuracy. ? 2024 IEEE.