计算机工程与应用2024，Vol.60Issue(17)：158-166,9.DOI:10.3778/j.issn.1002-8331.2305-0475
基于改进的YOLOv5s刨花板表面小目标缺陷检测算法
Small Defect Detection Algorithm of Particle Board Surface Based on Improved YOLOv5s
摘要
Abstract
An improved algorithm YOLOv5s-ATG for defecting particle board defects,based on YOLOv5s,is proposed to address the problem of poor precision in small target detection of particle board defect detection at present.To over-come the issue of particle board defects with small targets and large-scale changes,the original detector head is combined with the adaptive spatial feature fusion(ASFF)network to obtain better feature fusion.Transformer module is introduced into the backbone network,which uses a multi head self-attention mechanism to capture global spatial relationships and enhance the feature extraction capability of the network.For balancing the accuracy and complexity of the model,the Ghostv2 module is added to the backbone and neck of the network to improve the real-time performance of the algorithm.The experimental results show that the mean average precision(mAP)of the improved algorithm in the actual particle board defect data set can reach 0.901,which is 0.046 higher than the original model;for small target defect Gluespots,mAP is increased by 0.138.关键词
刨花板表面缺陷检测/YOLOv5s/深度学习/小目标检测/特征融合Key words
particle board surface defect detection/YOLOv5s/deep learning/small target detection/feature fusion分类
信息技术与安全科学引用本文复制引用
查健,陈先中,王文财,关淯尹,张洁..基于改进的YOLOv5s刨花板表面小目标缺陷检测算法[J].计算机工程与应用,2024,60(17):158-166,9.基金项目
国家自然科学基金(61671054). （61671054）
