Research on mining area obstacle detection model for edge computing

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 Bi