Anomaly detection method of inspection video for coal mine underground roadway deformatio

Abstract

When using intelligent video inspection technology for underground coal mine underground roadway deformation detection, the commonly used background difference algorithm cannot meet the requirements of inspection video background modeling due to the requirement of the input images having good temporal and spatial continuity. According to the characteristics of uniform speed, directional movement and periodic acquisition of video data of the deformation inspection robot in underground coal mine, an inspection video anomaly detection method is proposed. The method segments the inspection video with the inspection robot positioning information and extracts the corresponding key frames. Then the method establishes a background model based on the mean hash algorithm, and performs feature tracking on the frames in the background model to obtain correction. The method carries out a difference operation between the background model and the key frames to generate a binary mask and perform denoising and closed computing processing. Finally, the anomaly detection results are output and the key frames are updated. The experimental results show that the method can locate key frames and detect abnormal targets accurately under certain conditions, and the detection speed reaches about 50 frames/s.

Keywords

• roadway deformation detection
• inspection robot
• intelligent video inspection
• anomaly detection
• background difference