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 i...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-03, Vol.25 (3), p.3177-3190
Main Authors: Zhang, Xizheng, Cao, Xu, Zhang, Hui, Shen, Yongpeng, Yuan, Xiaofang, Cui, Zijian, Lu, Zhangyu
Format: Article
Language:English
Subjects:
6G mobile communication
6G-vehicle-to-everything
attention mechanism
Detection algorithms
Edge computing
Electric railroads
Feature extraction
Image edge detection
Mining electric locomotive
multi-scale feature prediction
Object recognition
Obstacle avoidance
obstacle detection
Transportation
Vehicle-to-everything
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3324145