###### 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. open-pit mine; unmanned driving; obstacle detection; improved YOLOv8s; model compression +
Footnote †: journal: applied sciences  
Most of the existing obstacle detection methods in engineering applications rely on laser radars to analyze the local features of the target to achieve the detection purpose. For example, [PERSON] et al. used multi-laser radars and combined features to fuse two 32-line radar point clouds for unmanned driving in a wild environment to achieve better negative obstacle detection effects [3]; [PERSON] and his team proposed an underground mine tunnel obstacle detection method based on a three-dimensional laser radar, citing the idea of a region growing algorithm combined with RANSAC to ensure driving safety during transportation [4]; [PERSON] et al. proposed a cross-modal fusion detection method of radar point cloud information and visual information, which suppressed irrelevant information in the radar point cloud and improved the detection accuracy of people, bicycles and motorcycles in autonomous driving [5]. Considering the poor robustness and generalization of the above radar-based obstacle detection methods, as well as the difficulty in achieving real-time data transmission, they are not suitable for complex open-pit mining scenarios. In contrast, deep learning-based image processing technology has demonstrated advantages in many fields due to the powerful feature learning ability and adaptability of convolutional neural networks [6]. [PERSON] et al. [7] proposed a multi-scale 3D detection method based on disparity clustering, which enhanced the detection of objects in traffic road environments, such as long distances, small sizes, and occlusion. [PERSON] et al. used the improved Tiny-Yolov3 model to detect obstacles in the driving process of mining trucks in a mine environment. Although the detection speed was slightly reduced, the detection accuracy was significantly improved [8]; [PERSON] et al.