Abstract:
Open-pit coal mines are characterized by complex environments that pose significant safety challenges. Hazardous driving behaviors among heavy machinery and transport vehicles are a major threat to operational safety, often leading to severe accidents and substantial losses. Factors such as poor road conditions and harsh working environments exacerbate these risks. At present, traditional safety monitoring methods such as manual inspection and vehicle-mounted video replay inspection are usually adopted in mining areas. However, these methods have disadvantages such as low efficiency, poor real-time performance, large manpower consumption and blind monitoring areas, which make it difficult to effectively deal with accident risks caused by hazardous driving behaviors. Advances in deep learning and computer vision offer promising solutions for improving the detection of hazardous driving behaviors. Compared to conventional methods, advanced detection systems enable real-time monitoring and alerts, enhancing coverage while reducing labor demands. Developing a detection system tailored to the conditions of open-pit mines is critical for ensuring safety and advancing intelligent mining practices. High parameter load, computational demands, and poor light inside the car at night leading to low detection accuracy in existing detection models are addressed. An improved object detection model, YOLOv8m–PSC, is proposed, incorporating partial convolution, self-attention mechanisms, and channel shuffle. A detection system based on the improved model is designed with a user-friendly graphical interface developed using PySide6, enabling rapid deployment in real-world scenarios and overcoming limitations of traditional monitoring methods. Experimental results show significant performance improvements: an average detection accuracy of 83.7% for hazardous behaviors, a 40.2% reduction in parameters, a 62.6% decrease in FLOPs, and a 10.7% increase in inference speed compared to the original model. The system efficiently detects fatigue and distracted driving in complex mining scenarios. The improved model advances object detection technology and provides practical insights for real-time hazardous behavior monitoring. Additionally, the system’s GUI design contributes innovative approaches to human-machine interaction in intelligent mining.