Mine Water Hazard Video Recognition Based on Residual Preprocessing and Temporal–Spatial Descriptors

Traditional water hazard monitoring often relies on manual inspection and water level sensors, typically lacking in accuracy and real-time capabilities. However, the method of using video surveillance for monitoring water hazard characteristics can compensate for these shortcomings. Therefore, this study proposes a method to detect water hazards in mines using video recognition technology, combining temporal and spatial descriptors to enhance recognition accuracy. This study employs residual preprocessing technology to effectively eliminate complex underground static backgrounds, focusing solely on dynamic water flow features, thereby addressing the issue of the absence of water inrush samples. The method involves analyzing dynamic water flow pixels and applying an iterative denoising algorithm to successfully remove discrete noise points while preserving connected water flow areas. Experimental results show that this method achieves a detection accuracy of 90.68% for gushing water, significantly surpassing methods that rely solely on temporal or spatial descriptors. Moreover, this method not only focuses on the temporal characteristics of water flow but also addresses the challenge of detection difficulties due to the lack of historical gushing water samples. This research provides an effective technical solution and new insights for future water gushing monitoring in mines.