基于目标检测网络的轮对踏面缺陷检测方法

针对传统图像处理算法难以快速、准确识别轮对踏面缺陷的问题,提出一种采用双深度神经网络对轮对踏面缺陷进行检测的算法。该双网络分为踏面提取网络与缺陷识别网络。根据踏面为大目标的特点,分析与测试SSD网络,并用该网络提取轮对图像中的踏面区域。为提高踏面缺陷识别效率,在提取出踏面图像后,针对踏面缺陷属于中、小目标的特点,对YOLOv3网络结构进行优化得到M-YOLOv3。实验测试表明:提取踏面区域时,SSD算法的精度均值(AP)最高,达99.8%;识别踏面缺陷时,M-YOLOv3的AP达89.9%,相较于原始YOLOv3,单张图像计算耗时减少7.1%,同时AP仅有0.6%的损耗。结果表明,所提算法具有较高的检测准确率。

Wheelset Tread Defect Detection Method Based on Target Detection Network

It is difficult to quickly and accurately identify wheelset tread defects using traditional image processing algorithms.We propose an algorithm to accomplish this using a dual deep neural network.The dual network is divided into a tread-extraction network and a defect-identification network.Based on the characteristics of the treads as a big target,we analyze and test the SSD network,and apply this network to extract the tread area from wheelset images.To improve the efficiency of tread defect recognition,after the tread image is extracted,we optimize the YOLOv3 network structure to obtain M-YOLOv3 for the characteristics of medium and small tread defect targets.The experimental results show that when extracting tread areas,the average precision(AP)of the SSD algorithm is the highest(99.8%).When identifying tread defects,the AP of the M-YOLOv3 network reaches 89.9%.Compared with the original YOLOv3,the image computing time of the M-YOLOv3 network is reduced by 7.1%,with the AP showing only a 0.6%loss.The results demonstrate the proposed algorithm’s high detection accuracy.