Lightweight real-time object detection for coal mine underground unmanned vehicles based on improved YOLOv8

TL;DRAbstract

Accurate object detection provides reliable environmental information for unmanned vehicles, serving as a critical safeguard to achieve safe and efficient operation of autonomous underground mining vehicles. However, traditional detection techniques face challenges such as low accuracy and poor real-time performance in complex underground coal mine environments characterized by insufficient lighting, coal dust interference, and dense distribution of small targets. This paper proposes an enhanced YOLOv8n-based algorithm incorporating three optimization strategies: 1) Replacing standard convolutional layers with depthwise separable convolutions (DSC) in the backbone network to significantly reduce parameters while maintaining feature extraction capabilities; 2) Developing a novel SPPF-LSKA module that integrates lightweight spatial pyramid pooling with attention mechanisms, achieving balanced multi-scale feature fusion and computational efficiency through ReLU activation optimization; 3)

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