基于改进YOLOv5的镍板表面缺陷检测方法  

Nickel Plate Surface Defect Detection Based on Improved YOLOv5

作　　者：谭沁源 唐勇 金岩 覃美满 吴伟 TAN Qinyuan;TANG Yong;JIN Yan;QIN Meiman;WU Wei(Changsha Institute of Mining Research Co Ltd,Changsha 410012,Hunan,China;National Engineering Technology Research Center of Metal Mining,Changsha 410012,Hunan,China;Jinchuan Group Co Ltd,Jinchang 737100,Gansu,China)

机构地区：[1]长沙矿山研究院有限责任公司,湖南长沙410012 [2]国家金属采矿工程技术研究中心,湖南长沙410012 [3]金川集团股份有限责任公司,甘肃金昌737100

出　　处：《矿冶工程》2024年第2期160-166,共7页Mining and Metallurgical Engineering

基　　金：湖南省科技成果转化及产业计划项目(2020GK2087)。

摘　　要：针对镍板表面缺陷检测智能化程度低的问题,提出了一种基于改进YOLOv5的镍板表面缺陷检测方法。首先,对图像增强后的镍板数据集通过K-means++重新聚类锚框,提高锚框对本文数据集的适应度。其次,在主干网络Backbone中加入CBAM注意力机制,通过空间与通道信息融合来加强对感兴趣区域以及不清晰目标的特征识别。最后,在边界框回归时引入EIoU损失函数代替原CIoU损失函数,有效提高回归收敛速度,从而提高模型检测速度。实验结果表明,在自建的镍板缺陷数据集上,改进后的模型检测准确率高于Faster R-CNN、SSD、YOLOv3、YOLOv5等模型,其平均精度均值达81.4%,检测速度达61帧/s,模型在提高检测精度的同时也很好地满足了对检测速度的要求。Aiming at low intelligence in nickel plate surface defect detection,a detection method based on improved YOLOv5 was proposed.Firstly,the image⁃enhanced dataset of nickel plate was re⁃clustered by K⁃means++to improve the adaptability of the anchor frame to the dataset.Secondly,the convolutional block attention module(CBAM)was added into the Backbone network to strengthen the feature recognition of interest areas and unclear targets by integration of spatial and channel information.Finally,an efficient IoU(EIoU)loss was introduced to replace the original CIoU loss during bounding box regression to effectively improve the convergence speed of regression,thereby increasing the model detection speed.The experimental results show that with the self⁃established dataset of nickel plate defect,the improved model,compared to Faster R⁃CNN,SSD,YOLOv3 and YOLOv5,has higher detection accuracy up to 81.4%on average,with detection speed reaching 61 frames per second.It is concluded that this model can not only improve detection accuracy,but also satisfy the requirements for detection speed.

关 键 词：表面缺陷 镍板 缺陷检测 图像处理 图像增强算法 YOLOv5 注意力机制 EIoU损失函数 准确率 平均精度 检测速度 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]