Detection of Train Bottom Parts Based on XIoU
Zhimeng Xin, Tongwei Lu, Xiuhua Li · 2019
Due to the complexity, diversity, or even small size of train bottom parts, the current object detection algorithm cannot identify it accurately. We propose a new method to solve the above problem. This method changes the computational way of loss based on Darknet-yolov3 for specific train bottom parts and improves the situation that the object detection network has low accuracy in detecting. IoU(Intersection over Union) can be directly used as a regression loss. However, IoU has a plateau making it infeasible to optimize in the case of nonoverlapping bounding boxes. We put forward XIoU to calculate the loss function. XIoU calculates the case when IoU is equal to zero and increases the amount of prediction sample for regression loss. The test set used the No.2 camera pictures provided by railway administration. Compared with Mobile-Net, Yolov3 and Yolov3-giou, the experimental results showed that the training results of XIoU were 10% higher than Mobile-Net and Yolov3 on mAP, and 0.2% higher than Yolov3-giou.