Research on the recognition and automatic reading method of pointer meter in complex environment based on WTOCA_ResNet
Min Wan, Yang Yang · Engineering Research Express · 2025
Abstract Pointer-type instruments are widely used in various industrial fields due to their simplicity, low cost, strong anti-interference capability, and durability. However, manually reading and recognizing these instruments can be complex and labor-intensive. To address this challenge, this paper proposes an automated method for pointer meter recognition and reading in complex environments, based on the WTOCA_ResNet framework, which integrates the WTFD, OCA, and ResNet modules. First, the improved WTOCA_ResNet model is employed to remove rain streaks from the input images. The instrument panel is then detected using YOLOv8. Tilt correction and dial region extraction are performed using a combination of SIFT feature matching, the RANSAC algorithm, perspective transformation, and Hough circle detection. To enhance dial details, bilateral filtering and CLAHE (Contrast Limited Adaptive Histogram Equalization) are applied. YOLOv8 is further utilized to locate the center of the dial, while the pointer tip is accurately identified using a combination of masking and Hough line detection. An improved multi-scale template matching algorithm is introduced to locate the zero scale, and the final reading is calculated based on angular measurements. Experimental results demonstrate that the WTOCA_ResNet model achieves state-of-the-art performance in image quality assessment after rain removal. The improved multi-scale template matching method reaches a recognition accuracy of 97.5%, and the overall error of the proposed method remains within 0.62% under real-world complex field conditions, indicating high accuracy, robustness, and practical applicability.