An OBB Detection Algorithm of Maintenance Components Based on YOLOv5-OBB-CR

Abstract

By detecting the position of maintenance components in real-time, maintenance guidance information can be superimposed and important operational guidance can be provided for maintenance personnel. The YOLOv5-OBB-CR real-time detection algorithm is proposed for maintenance component with orientation bounding box based on improved YOLOv5-OBB. The C3 module in the original network is improved to CReToNeXt, which can more effectively enhance the network's ability to learn image features. Considering that the network learning is the labeled rotation box information, the original Loss function CIoU is improved to SIoU with angle loss information, and the improved Loss function can more effectively describe the regression of the target box. The demonstration shows that the mAP@.5 0.95 of YOLOv5-OBB-CR-s (SIoU) is 85.6%, which is 6.7% higher than the original YOLOv5 OBB algorithm.