Looseness detection of fasteners on conductor rails of medium and low speed maglev railways based on deep convolutional networks - LI Cheng; CHEN Jianxiong; LIN Jun; KANG Gaoqiang (机车电传动)
To deal with the looseness of fasteners on the conductor rails of the medium and low speed maglev railways, a detection algorithm based on deep convolutional networks was proposed for loose detection of two kinds of fasteners, i.e. the base mounting bolt and the connecting plate screw. The first step was to locate the fastener area on the conductor rail for the sake of eliminating background interference; the second was to analyze any change of the fastener position for looseness detection. This algorithm was executed and verified through deep convolutional networks. Firstly, the areas of these two kinds of fasteners were located through YOLO V2 network; secondly, segmentation was conducted at the edge of the connecting plate, the insulator, the bolt and the screw, and the head of the connecting plate screw through the Mask R-CNN network simultaneously; finally, looseness of fasteners was detected by judging whether the position of any segmented part was changed. The proposed defect detection algorithm was tested using the data of the medium and low speed maglev system in Changsha, which revealed a looseness detection accuracy of over 90% for both the base mounting bolts and the connecting plate screws, and a recall rate of over 94%. The test results show that the method presented in this paper can accurately identify the loosened fasteners on the conductor rails of the medium and low speed maglev railways.