Abstract: In recent years, with the rise of autonomous driving technology for mining trucks, detecting obstacles on mining roads has become crucial. Object detection models based on deep learning have been applied to significant effect in detecting obstacles on mining roads, thereby providing possibilities for the improvement of autonomous driving technology for mining trucks. To address the issues of large algorithms and high deployment costs associated with existing models for mining obstacle detection, an improved YOLOv8 model tailored for edge computing platforms is proposed. This model is optimized for deployment on resource-constrained edge computing devices to achieve rapid and accurate obstacle detection. In this model, during the feature extraction stage, depthwise separable convolutions and channel attention mechanisms are introduced to enhance the model’s ability to extract overall features of obstacles, thereby improving the detection accuracy of obstacles of various sizes. In the feature fusion stage, a BiFPN network structure is employed to lightweight the backbone network and adaptively adjust fusion weights, reducing redundant information and enhancing feature representation. The detection head is redesigned using local convolution PConv to reduce network parameter size and improve detection efficiency. Finally, by introducing the Inner-CIoU function for bounding box loss optimization, the model convergence speed is accelerated, and bounding box localization effectiveness is enhanced. Experimental results demonstrate that on the mining obstacle dataset used, while maintaining a decrease of only 0.05 in mAP@0.5, the model parameters are reduced by 44%, and the inference time is reduced by 34%. Compared to other mainstream detection networks, this model exhibits faster detection speed on the hardware devices used in the experiments and better balances the requirements of accuracy and lightweight, providing a feasible solution for the practical deployment of obstacle detection models.