A method of railway fastener defect detection based on ResNet-SSD.

Missing and broken states of railway fasteners are directly related to driving safety. A deep network image detection method for railway fasteners based on residual network-single shot multibox detector (ResNet-SSD) is proposed to improve detection performance. Firstly, virtual image technology is used to simulate the defect states of fasteners, so as to expand feature information and overcome the imbalance problem of various fastener images. Secondly, ResNet is used to construct feature extraction network, and SSD is used for target detection to form ResNet-SSD network to detect the defect states of fasteners. Experimental results show that the ResNet-SSD has faster convergence speed and mean average precision (mAP) is increased by 3.06% from 95.47% to 98.53% after using the virtual image to expand the field collected fastener set; The detection accuracy of ResNet-SSD can reach 98.5% when the recall is 98.8%, which is higher than that of VGG16-SSD and the same as that of Faster-RCNN when intersection over union(IoU) is 0.5; The detection speed of this method is 6 times that of Faster RCNN and 1.5 times that of VGG16-SSD. Therefore, this method can meet the needs of real-time detection of railway fasteners. [ABSTRACT FROM AUTHOR]