计算机与现代化Issue(9)：107-113,7.DOI:10.3969/j.issn.1006-2475.2024.09.018
基于改进YOLOv8s的摄像头模组缺陷检测
Camera Module Defect Detection Based on Improved YOLOv8s
摘要
Abstract
Aiming at the problems of the great change of defect size,unclear contour and high missed detection rate of small tar-get defects in camera module defect detection,an improved YOLOv8s algorithm is proposed.Firstly,the small target detection layer is added to improve the detection performance of small targets.Secondly,BiFormer is introduced to improve the C2f module in the backbone network,and the C2f-Bif module is proposed to enhance the ability of the network to extract image features.Then,the H-SPPF(Hybrid Fast Space Pyramid Pooling)module is proposed to enhance the ability of the network to capture lo-cal and global information.Finally,the parameter-free SimAM attention mechanism is added to suppress the non-target back-ground interference information and improve the attention of the target.The experimental results show that the average accuracy of the improved YOLOv8s algorithm for camera module defect detection reaches 87.2%under the condition of reducing the num-ber of model parameters,which is 3.2 percentage points higher than that of the YOLOv8s algorithm.The detection speed reaches 55 FPS,which meets the factory's real-time detection requirements for camera module defects.关键词
深度学习/YOLOv8s/缺陷检测/摄像头/BiFormer/注意力机制Key words
deep learning/YOLOv8s/defect detection/camera/BiFormer/attention mechanism分类
信息技术与安全科学引用本文复制引用
张泽,张建权,周国鹏..基于改进YOLOv8s的摄像头模组缺陷检测[J].计算机与现代化,2024,(9):107-113,7.基金项目
湖北省科技计划项目(2020BGC028) （2020BGC028）
湖北省重点研发计划项目(2021BGD022,2022BBA026) （2021BGD022,2022BBA026）
