Abstract
Aiming at the problem of low recognition accuracy of digital display meter readings when the inspection robot performs inspection tasks, a YOLOv5-based digital display meter detection and recognition algorithm for distribution cabinets is proposed. For the digital display meter image captured by the inspection robot spherical camera, the YOLOv5 model is used to locate the target character area. After scale normalization, image correction, filtering and noise removal and other image pre-processing operations, the character recognition is completed in combination with the traditional machine vision algorithm, and the reading results are automatically output. For the characteristics of digital tube characters, the threading method, support vector machine algorithm and PaddleOCR algorithm are used for comparison, and a suitable algorithm model is selected to recognize numeric, alphabetic and decimal point characters. The experimental results show that the accuracy of detecting and identifying digital display meters using the YOLOv5 model and PaddleOCR algorithm is 95.3%.
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Zhou, Y., Zhang, Y., Wang, C., Sun, S., Wang, J. (2022). Detection and Identification of Digital Display Meter of Distribution Cabinet Based on YOLOv5 Algorithm. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_23
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DOI: https://doi.org/10.1007/978-981-19-6142-7_23
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