基于改进YOLOv7的金属表面小缺陷检测研究

Small defect detection of metal surfaces based on improved YOLOv7

传统的金属表面缺陷检测是通过人工目测完成的,由于人工目测方法存在效率低下、漏检率高、劳动强度大等缺点,难以满足金属表面缺陷检测的效率和精度要求。针对工业生产过程中金属表面的小缺陷人工检测效率低等问题,提出了一种基于改进的YOLOv7算法的金属表面小缺陷检测方法。首先,建立了包含5种金属表面小缺陷的数据集;然后,设计了扩散卷积,利用步长改变了卷积核中特征点的间距,扩大了卷积层的感受野;设计了方向注意力模块,通过分割输入特征图,在水平方向和垂直方向上进行了特征提取,在通道维度上引入了注意力机制,根据通道的权重,完成了对输出通道数目的重新调整,增强了YOLOv7对小缺陷的位置感知;最后,研究了不同算法在金属表面小缺陷数据集上的目标检测结果,设计了消融实验,对改进策略进行了性能分析。研究结果表明:在相同训练策略下,与传统的YOLOv7算法模型相比,改进后的YOLOv7算法对小缺陷的检测效率为91 fps,平均检测精度为88.0%,较原模型提高了3.6%。在实际生产中可以采用该方法精确检测复杂背景下的金属表面小缺陷。

The traditional metal surface defect detection is completed by manual visual inspection.Because of the shortcomings of manual visual inspection,such as low efficiency,high missed detection rate and high labor intensity,it is difficult to meet the efficiency and accuracy requirements of metal surface defect detection.Aiming at the problem of low efficiency for manual detection of small defects on metal surfaces in industrial production process,a method for detecting small defects on metal surfaces was proposed by enhancing YOLOv7 algorithm.Firstly,a dataset containing five types of small defects on metal surfaces was established.Then,a diffusion convolution was designed.The spacing of the feature points in the convolution kernel was enlarged using the step size to expand the receptive field of the convolution layer.Then,a directional attention module was designed.After the input feature map was segmented in horizontal and vertical directions,feature extraction was performed on the feature map.The attention mechanism was introduced in the channel dimension.According to the weights of the channels,the readjustment of the number of output channels was completed to augment the positional awareness for target of small defects in YOLOv7.Finally,the results of target detection from different algorithms on the dataset of small defects on metal surfaces were investigated,and ablation experiments were designed to carry out the performance analysis of the different improved strategy.The experimental results show that,under the same training strategy,comparing with the traditional YOLOv7 algorithm model,the detection efficiency of the improved YOLOv7 algorithm is 91 fps for small defects,and the average detection accuracy is 88.0%,which is 3.6% higher than that of the original model.It can be seen that this method can accurately detect small defects on metal surfaces under complex backgrounds in actual production.