Design and Development of “Smart Metre Reading” and Monitoring System From Digital Metre Dataset

This research explores the potential for reducing the cost of advanced metering infrastructure (AMI) by leveraging raw images captured from the screens of digital energy instruments. The study focuses on the extraction of text and the recognition of 7 -segment numbers through optical character recognition (OCR) techniques. The proposed OCR-based dataset holds promise for facilitating fully automated electricity billing processes. Additionally, the research highlights the impact of utilizing high-resolution smart metre data in enhancing the efficiency, reliability, and resilience of distribution power grids. The “digital metre” dataset, comprised of images of digital energy metres, serves as a valuable resource for advancing the field. The study introduces a methodology for automatically reading dial-metre digits through the utilization of a deep learning model based on the YOLOv5 architecture. Evaluation metrics such as precision, recall, and mean Average Precision (mAP) are employed to assess the model's effectiveness. This research underscores the efficacy of the suggested network model in executing object-detection tasks, showcasing superior recall, mAP, and precision in the context of smart metre reading.