A Real-Time Negative Obstacle Detection Method for Autonomous Trucks in Open-Pit Mines

Abstract

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds.