Instrument Detection and Reading Based on Arbitrary Quadrilateral
Huihui Li, Yulin Feng, Yi Fan Zhao, Qianru Wen, Hang Liu · IEEE Transactions on Instrumentation and Measurement · 2024
Pointer instruments are widely used in various fields due to their simplicity, low cost, strong anti-interference capabilities, and durability. However, manual reading and recognition of these instruments can be complex and labor-intensive. Existing meter recognition algorithms have limitations in dial extraction and reading calculation, which restrict their practical applications. In this article, we propose an instrument detection method based on arbitrary quadrilateral positioning. It directly locates key areas such as pointers and scales, eliminating irrelevant information and simplifying the reading process. By applying perspective transformation to the detected quadrilateral, the instrument is transformed into a front-view state, eliminating the need for time-consuming postprocessing operations. We also propose a reading algorithm that handles missing scales, improving reading accuracy. Experimental results demonstrate that our method outperforms existing algorithms in terms of detection and reading accuracy. Using a self-built instrument dataset, the mean average precision (mAP) is 92.4, and the absolute error of the readings is less than 0.02. It is particularly effective in challenging scenarios involving tilt, rotation, changing lighting, and small targets, making it suitable for real-world applications.