基于Faster R-CNN的钢轨表面缺陷识别研究

doi:10.16180/j.cnki.issn1007-7820.2020.09.011

Abstract

Abstract:

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.

Key words: rail surface defect, preprocessing, radon transform, grey value, regional proposal network, Faster R-CNN networks

CLC Number:

TP278

SU Ye,LI Jun,YANG Haima,LIU Jin,JIANG Shenghua. Research on Rail Surface Defect Recognition Based on Faster R-CNN[J].Electronic Science and Technology, 2020, 33(9): 63-68.

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References 16

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钢轨表面缺陷		平均检测时间/s
疤痕	91.43	0.32
裂缝	91.09
划伤	92.78
磨损	92.46

检测方法	四种缺陷的平均识别准确率/%
检测方法	疤痕	裂缝	划伤	磨损
BP神经网络	87.92	85.26	88.74	83.42
文献[16]	84.35	79.33	82.46	83.75

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

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