基于Faster R-CNN的钢轨表面缺陷识别研究

Research on Rail Surface Defect Recognition Based on Faster R-CNN

外界因素常会干扰钢轨表面缺陷检测仪器,导致其精度和效率降低。文中研究了一种基于Faster R-CNN网络检测钢轨表面缺陷的方法。该方法将预处理后的图像进行反转,利用Radon变换实现钢轨图像的投影。投影曲线中,利用钢轨长度为定值且灰度值小于图像平均值的特性,完成对钢轨表面区域的提取。然后通过区域建议网络提取候选区域,并与Fast R-CNN网络的区域建议对比分析,完成Faster R-CNN网络对钢轨的表面缺陷检测。试验数据表明,裂缝、疤痕、磨损和划伤4种缺陷的识别精度分别为92.17%、91.85%、93.45%和93.27%,证明使用该方法能够高效而又准确地识别钢轨的表面缺陷。

External factors usually have effects on the instrument used to detect rail surface defect,resulting in poor accuracy and efficiency of instrument.For this problem,a method for detecting rail surface defects based on the Faster R-CNN network is investigated.The method reverses the preprocessed image and realizes the projection of rail image with Radon transform.In the projection curve,the rail surface area is extracted by using the characteristics that the rail length is fixed and the gray value is less than the average value of the image.Then,the candidate region is extracted through the regional proposal network and compared with the regional recommendation of Fast R-CNN network for the detection of surface defects of rail by the Faster R-CNN networks.According to the test data,the accuracy of crack,scar,abrasion and scratch is 92.17%,91.85%,93.45%and 93.27%respectively,which verifies the efficiency and accuracy of the proposed method in identify the surface defects of rail.