Research on Lightweight Open-Pit Mine Driving Obstacle Detection Algorithm Based on Improved YOLOv8s

Abstract

The road environment of open-pit mines is complex and unstructured. Unmanned construction machinery driving faces huge challenges. Improving the accuracy and speed of obstacle detection during driving is of great significance to ensuring the safety of mine automation construction and improving the efficiency of overall unmanned operations. In view of the fact that the current obstacle detection algorithm struggles to strike a balance between high precision and real-time performance, and there are problems such as difficulty in model deployment or unsuitability for practical applications, a lightweight open-pit mine driving obstacle detection algorithm based on improved YOLOv8s is proposed, which is committed to improving the driving safety of unmanned engineering machinery in open-pit mines. In order to enhance the ability of the backbone to capture features, the idea of the guidance module (CGBlock) of contextual information is introduced to construct a new CGC2f module; the efficient squeeze excitation (ESE) attention mechanism is embedded in the feature fusion layer to make the model pay more attention to the channels containing important feature information; in order to enhance the model’s learning ability for obstacles of different sizes in the open-pit mine, a more suitable dynamic head network (DyHead) is used at the output end; in order to further improve real-time performance, the layer-based adaptive amplitude pruning (LAMP) score algorithm is used to prune redundant weight parameters. To verify the effectiveness of the algorithm in this paper, an experimental verification is carried out on the constructed open-pit mine driving obstacle dataset. The results show that compared with YOLOv8s, the mAP50 of this algorithm reaches 95.3%, the detection speed is increased by 40.2%, the model parameters are reduced by 71.2%, and the calculation amount is reduced by 73.7%. It meets the requirements of real-time and high-precision obstacle detection in open-pit mine driving and provides technical support for smart mine driving.