空军工程大学学报2025，Vol.26Issue(5)：11-21,11.DOI:10.3969/j.issn.2097-1915.2025.05.002
CSM-YOLO:一种面向飞机表面缺陷检测的轻量化高精度网络
CSM-YOLO:A Lightweight and High-Precision Network Geared to Defects on Aircraft Surface in Detecting
摘要
Abstract
In view of the problems that in detecting defects often show on surface of aircraft by using exist-ing visual-based method,there remains a low accuracy in detection,high parameters and expend time is more in computational consumption,and such a method is difficult to balance accuracy improvement with model lightweight,a new high-precision and lightweight aircraft surface defect detection method,called CSM-YOLO,is proposed.First,C2f module in the backbone network replaced by C2f-SCSA module is to dynamically enhance multi-scale features and improve the model's ability to capture,extract,and utilize key feature information,solving the feature loss caused by down-sampling.Secondly,the Slim-Neck fea-ture fusion network is improved with cross-layer connection and applied to the model neck,realizing the boosted computational efficiency and the detection accuracy and simultaneously cutting the information loss.Lastly,MPDIoU Loss is used to enhance bounding box regression accuracy and improve the detection precision of small target defects and cut false and missed detections.The experiments show that the CSM-YOLO enables to achieve high precision and lightweight.A maximum detection accuracy of 88.34%on surface defects can be reached,and there is a 2.92%improvement compared with the baseline YOLOv8n model,and the improvement in computation is obvious compared with the YOLOv3-tiny,the YOLOv5n,the YOLOv5s,the YOLOv7-tiny,the YOLOv9t and the YOLOv12n respectively.In aspect of model pa-rameters and computation,for the CSM-YOLO,there is a parameter of 2.67×106/s and a computational cost of 7.68×109/s,reducing the baseline YOLOv8n by 0.34×106/s and 0.41×109/s respectively.And the CSM-YOLO is balancing the accuracy improvement with the model lightweight.Moreover,the CSM-YOLO delivered significant performance gains on the aircraft surface defect detection dataset,offering an effective automated solution for surface defect detection.关键词
飞机表面缺陷检测/YOLOv8/模型轻量化/空间-通道协同注意力/MPDIoU损失/Slim-NeckKey words
aircraft surface defect detection/YOLOv8/model lightweighting/spatial-channel cooperative attention/MPDIoU loss/Slim-Neck分类
航空航天引用本文复制引用
介战铎,张争明,黄浩然,郝明,赵俭邦..CSM-YOLO:一种面向飞机表面缺陷检测的轻量化高精度网络[J].空军工程大学学报,2025,26(5):11-21,11.基金项目
国家自然科学基金(522750696) （522750696）
