软件导刊2026，Vol.25Issue(4)：98-103,6.DOI:10.11907/rjdk.251095
基于CSE-YOLO的改进铝型材表面缺陷检测模型
Improved Surface Defect Detection Model for Aluminum Profile Based on CSE-YOLO
刘子龙 1庞明 1窦建明1
作者信息
- 1. 兰州交通大学 机电工程学院,甘肃 兰州 730070
- 折叠
摘要
Abstract
To address the challenge of balancing model complexity,detection speed,and accuracy in aluminum profile surface defect detec-tion,an enhanced YOLOv5-based model named CSE-YOLO was developed.First,a novel CCFBiFPN architecture was constructed by inte-grating CCFF with BiFPN,which strengthened multi-scale feature fusion while reducing model complexity.Second,an SDI module incorporat-ing hybrid GSConv was implemented in the feature fusion stage to improve feature extraction capability.Finally,an ECA attention mechanism was embedded in the backbone network to enhance detection precision and overall performance.Experimental results demonstrated that com-pared with the baseline YOLOv5s model,the proposed approach achieved 5%and 1.3%improvements in mAP@0.5 and mAP@0.5:0.9 met-rics,respectively.Concurrently,parameter count and FLOPs were reduced by 4.3%and 4.4%,with the frame rate increasing from 140 to 143 FPS.The optimized architecture effectively balanced performance and complexity,demonstrating substantial practical value for industrial qual-ity inspection applications.关键词
铝型材/表面缺陷检测/YOLOv5/注意力机制/特征融合Key words
aluminum profile/surface defect detection/YOLOv5/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
刘子龙,庞明,窦建明..基于CSE-YOLO的改进铝型材表面缺陷检测模型[J].软件导刊,2026,25(4):98-103,6.
