Abstract: The deep mining working face is characterized by high in-situ stress and high water pressure, making water inrush hazards increasingly severe. With the rapid development of artificial intelligence and the continuous advancement of smart mining construction, deep learning and computer vision have been widely applied in mine safety monitoring, providing new approaches for mitigating water inrush risks. Aiming at the need for real-time water inrush early warning in deep mines, this study innovatively integrates deep learning, machine vision, and electrical monitoring, proposing a real-time two-stage electrical monitoring method based on machine vision. The monitoring process consists of two consecutive stages: the visual recognition stage and the analysis and decision-making stage. In the visual recognition stage, the lightweight deep learning model YOLO11s is used to rapidly and accurately identify low-resistivity regions in electrical images and obtain their spatial coordinates. These coordinates are then passed into the analysis and decision-making stage, where an OpenCV-HSV color filtering algorithm analyzes the identified low-resistivity regions from three aspects—quantity, area ratio, and color depth—to comprehensively determine whether the regions pose potential water inrush risks, triggering multi-level warning responses through an automatic decision mechanism. Results show that the YOLO11s model can accurately assess whether water sources may cause water inrush hazards and automatically trigger multi-level warnings, overcoming the limitations of other monitoring methods in real-time detection, disaster evolution analysis, and early warning. The YOLO11s model achieved a mean Average Precision (mAP) of 90.2%, with an average inference time of 34.6 ms per frame and an average processing rate of 28 frames per second (FPS), outperforming YOLOv8, YOLOv5, and Fast R-CNN in detection accuracy and speed. The OpenCV-HSV color filtering algorithm required only 7 ms per image for analysis and decision-making, realizing real-time early warning of water inrush hazards.