改进YOLOv5的瓷砖表面缺陷检测

Detection of Ceramic Tile Surface Defects Based on Improved YOLOv5

现有瓷砖表面缺陷检测存在识别微小目标缺陷能力不足、检测速度有待提升的问题,为此本文提出了基于改进YOLOv5的瓷砖表面缺陷检测方法.首先,由于瓷砖表面缺陷尺寸偏小的特性,对比分析YOLOv5s的3个目标检测头分支的检测能力,发现删除大目标检测头,只保留中目标检测头和小目标检测头的模型检测效果最佳.其次,为了进一步实现模型轻量化,使用ghost convolution和C3Ghost模块替换YOLOv5s在Backbone网络中的普通卷积和C3模块,减少模型参数量和计算量.最后,在YOLOv5s的Backbone和Neck网络末端添加coordinate attention注意力机制模块,解决原模型无注意力偏好的问题.该方法在天池瓷砖瑕疵检测数据集上进行实验,实验结果表明:改进后的检测模型的平均精度均值达66%,相比于原YOLOv5s模型提升了1.8%;且模型大小只有10.14 MB,参数量相比于原模型减少了48.7%,计算量减少了38.7%.

The existing detection method of ceramic tile surface defects has the problem of insufficient ability to identify small target defects,and the detection speed needs to be improved.Therefore,this study proposes a ceramic tile surface defect detection method based on improved YOLOv5.Firstly,due to the small size of ceramic tile surface defects,the detection abilities of three target detection head branches of YOLOv5s are compared and analyzed.It is found that the effectiveness of the model that removes the large target detection head and retains only the medium and small target detection heads is optimal.Secondly,to further realize the lightweight of the model,the study applies ghost convolution and C3Ghost modules to replace the ordinary convolution and C3 modules of YOLOv5s in the Backbone network,thus reducing the number of model parameters and the calculation amount.Finally,the coordinate attention mechanism module is added at the end of the Backbone and Neck networks of YOLOv5s to solve the problem of no attention preference in the original model.The proposed method is tested on the Tianchi ceramic tile defect detection dataset.The results show that the mean precision of the improved detection model averages 66%,which is 1.8%higher than the original YOLOv5s model.Besides,the size of the model is only 10.14 MB,and the number of parameters and the calculation amount is reduced by 48.7%and 38.7%respectively compared with the original model.