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Robust and general reading recognition for pointer meters based on key point detection and large language model
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DOI：10.1088/1361-6501/adb067.png)
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Abstract
En 中文
To meet the requirements of smart factories and intelligent industrial settings, it is of great significance to develop a reading recognition method for pointer meters. As existing methods lack the robustness necessary for complex environments and are overly specialized for specific meter types. In this paper, a robust and general method for pointer meter reading recognition is introduced. Our method demonstrates adaptability to a wide range of pointer meter types and maintains effectiveness under extreme conditions. Initially, a Tiny-YOLO object detection network is utilized to distinguish meters from the background. Subsequently, a multi-head key point detection network with a fusion strategy based on Swin Transformer is employed to identify four key points. The unique multi-head structure facilitates the key point detection of various image quality. The percentage indication is calculated using the angular method based on the key points. The numerical indication and the units come from the refined optical character recognition network by a large language model. Several extreme experiments have been conducted to demonstrate the robustness of our methods, with an average quoted error of less than 0.24%.
Keywords:
pointer meter
reading recognition
deep learning
computer vision
large language model
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