Abstract: Due to changes in mining field geological conditions, fluctuations in pump station pressure, and errors in automated following system, hydraulic supports may experience a loss state during the automatic frame movement process. Manual monitoring of support loss significantly affects the efficiency of automated following at the working face. However, real-time loss state monitoring methods for hydraulic supports based on sensors and perception information often lack stability and reliability. To address these issues, a visual automatic detection method for hydraulic support loss state was proposed. First, YOLOv8 was used to segment key areas of the working face from real-time monitoring video images. By thoroughly analyzing the internal features of the working face images, the contours and location information of hydraulic support bases and push rods were accurately obtained. The location information of different hydraulic support bases and push rods was analyzed to determine the number of each hydraulic support in the monitoring video. Then, the local image of the smallest base region between adjacent hydraulic supports was extracted. A ResNet50 convolutional network, incorporating multi-scale feature fusion, was employed to extract features from the local image of the base, obtaining multi-scale fusion feature information of the image. This feature information was then mapped to a classification space to derive the probability distribution of different hydraulic support states. The support's normal movement or loss state was determined based on these probabilities, and the support number information was combined to identify the hydraulic support experiencing a loss state. Experimental results showed that the average segmentation accuracy of key areas at the working face using monitoring video was 0.98, achieving structured extraction of target regions. The automatic identification accuracy for support numbers was 98.78%, providing precise support number information for hydraulic support loss state detection. The average detection accuracy of the visual automatic detection method for hydraulic support loss state at the working face was 99.17%, and the single frame image processing time is 36 ms, meeting the real-time and reliability requirements for detecting support loss states in coal mining face AI video monitoring systems.