Spot Image Segmentation of Lifting Container Vibration Based on Improved Threshold Method and Mathematical Morphology

Because the mine is damp and dark, it is not easy to detect the rigid tank channel’s structural failure directly. Therefore, we judged the tank channel’s surface condition by detecting the magnitude of the vibration displacement of the lifting container. In our study, we used a laser vision system to measure the structural vibration displacement. In order to accurately segment the laser spot information from the vibration image, we proposed an approach that links the relationship between the gray value of the area adjacent to the threshold point and the background’s gray value to the target in the image. We used MCE to evaluate the segmentation effect of threshold segmentation and verified the improved algorithm’s accuracy by detecting the pixel centroid of laser spots. Results show that the improved algorithm in our study has the best threshold segmentation effect, the error classification can be close to 0.0003, and the minimum deviation of the obtained vibration displacement is close to 0.1 pixels, which can realize the accurate extraction of the vibration signal of the vertical shaft tank. The novelty of this method lies in the accurate threshold segmentation and noise reduction processing of the laser speck vibration image under various interference environments in the operation of the mine hoisting system and the accurate acquisition of vibration signals. The research work provides a basis for the accurate evaluation of mechanical faults of automation technology.