Abstract
The recognition of analog pointer meters under nature environment is commonly a challenge task due to many influences like types of meters, shooting angle, lighting condition, etc. Most existing recognition algorithms consist of multiple steps including the detection and extraction of dial, scale marks and pointers followed by reading calculation, which is complex and sensitive to image quality. To address this issue, a one-stage, end-to-end recognition method for pointer meter based on deep regression is proposed in this paper. The proposed method simultaneously locates the position of the end point of a pointer, obtains a meter reading and determines whether the pointer exceeds the normal range through a fully convolutional neural network. Without complicated image pre-processing and post-processing, the algorithm can read multiple meters in one image just through a simple one-round forward inference. Experimental results show that the recognition accuracy achieves 92.59% under ±5% reading error, and the processing speed reaches approximately 25 FPS on a NVIDIA GTX 1080 GPU.
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Chao, Z., Mao, Y., Han, Y. (2021). Reading Pointer Meter Through One Stage End-to-End Deep Regression. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_29
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DOI: https://doi.org/10.1007/978-3-030-88013-2_29
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