融合注意力机制与线激光辅助的输送带缺陷检测网络  

Conveyor Belt Defect Detection Network Combining Attention Mechanism with Line Laser Assistance

作　　者：宋震 王纪强 侯墨语 赵林[1] SONG Zhen;WANG Jiqiang;HOU Moyu;ZHAO Lin(Laser Institute,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250104,China)

机构地区：[1]齐鲁工业大学(山东省科学院),山东省科学院激光研究所,济南250104

出　　处：《计算机科学》2024年第S01期569-574,共6页Computer Science

基　　金：国家重点研发计划(2022YFB3207602);山东省自然科学基金重点项目(ZR2020KC012)。

摘　　要：针对输送带缺陷种类繁多、缺陷特征像素占比小以及传统算法检测精度低的问题,采用随机仿射变换,扩充样本数据集;分析各通道间的关联关系及其贡献值对模型特征提取的影响,提出了一种通道关联加权注意力机制,利用关联卷积及全连接方式计算通道关联度及贡献权值,调整相应通道信息占比,提升模型检测精度;分析了上采样以及卷积块对输出特征图大小的影响,改进原特征金字塔特征卷积块及上采样结构,提高算法对小目标的特征提取以及缺陷检测能力;最后在输送带缺陷数据集上进行测试。结果表明:改进算法模型能对输送带典型的异物插入、破损、撕裂等缺陷特征进行有效识别,识别精准度可达99.7%,召回率大于99.5%,平均精度均值达到99.5%。Aiming to the problems of a wide variety of conveyor belt defects,a small proportion of defect feature pixels,and the low detection accuracy of traditional algorithms,random affine transformation is used to expand the sample dataset.The influence of the correlation between each channel and its contribution value on the model feature extraction is analyzed,and a channel correlation weighted attention mechanism is proposed.The correlation degree and contribution weight of each channel are calculated by correlation convolution and full connection,and the proportion of corresponding channel information is adjusted to improve the detection accuracy of the model.The influence of upsampling and convolution block on the size of the output feature map is analyzed.The original feature pyramid feature convolution block and upsampling structure are improved to enhance the feature extraction and defect detection ability of the algorithm for small targets.Finally,the test is conducted on the conveyor belt defect data set.The results show that the improved algorithm model can effectively identify the typical defect features such as foreign body insertion,breakage,and tearing of the conveyor belt.The recognition precision can reach 99.7%,the recall rate is increased to 99.5%,and the mean average precision is 99.5%.

关 键 词：皮带缺陷检测 深度学习 通道关联加权处理 小目标检测层 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]