An Intelligent Obstacle Detection for Autonomous Mining Transportation With Electric Locomotive via Cellular Vehicle-to-Everything and Vehicular Edge Computing

The tremendous revolutionary progress of cellular vehicle-to-everything (C-V2X) and vehicular edge computing (VEC) technologies provide new opportunities to overcome the autonomous transportation issue of the mining electric locomotives (MELs), in which the accurate and fast detection of obstacles is crucial for the safe operation. With the VEC and C-V2X, we proposed a new high-precision obstacle detection strategy for MELs (MEL-YOLO). Firstly, we investigated the convolutional attention mechanism integrated into the path aggregation network of the Neck layer to strengthen the feature extraction capabilities. Secondly, we added a small-object oriented prediction layer in the Head to form the multi-scale feature prediction. Thirdly, we introduced a more efficient loss function to alleviate the gradient explosion problem in the feature transfer. Finally, we utilized the K-means++ optimization to derive the anchor boxes matchable with the dataset, which was collected and created by featuring different scenes to train validate the model. The MEL-YOLO was compressed by BN layer pruning and implemented on the edge device in a 6G/B5G based-V2X environment. Experimental results verify that the MEL-YOLO can effectively detect obstacles and significantly improve detection accuracy for small obstacles, computationally increasing mAP by 3.3% to original model, while maintaining detection speed and model size nearly unchanged.

I. Introduction

One significant mode of autonomous rail transportation in underground mining industry is by the mining electric locomotives (MELs), which are essential in transporting equipment, ore deposits, and personnel [1]. These locomotives operate in challenging conditions with poor lighting and high levels of dust, creating significant challenges for safe operation. The development of autonomous MELs can significantly reduce the risk of traffic accidents, minimize casualties, protect the lives and safety of underground personnel, and deliver significant economic and social benefits [2], [3]. Nevertheless, obstacles such as falling rocks, workers, construction materials and other obstacles inevitably arise in the rail of MELs. Therefore, detecting the rail obstacles has become an essential problem to address the issue of MELs automation, which can lead to enhanced safety, greater efficiency and improved economic returns.