LAUSR

Reading power equipment meters often requires loads of manpower, which is a trivial, repetitive, and error-prone task. While conventional automated recognition methods using computer vision (CV) techniques are inflexible under diverse scenarios, in this article, we propose a lightweight meter recognition method that combines deep learning and traditional CV techniques for automated meter reading. For meter detection, an adaptive anchor and global context (GC) module are deployed to improve the feature extraction ability of lightweight backbone without increasing computational cost. Then, an feature pyramid network (FPN) and a path aggregation network (PANet) are developed to realize the information interaction between different feature layers and achieve multiscale prediction. Our method also includes a multitask segmented network to read the detected meters, accelerating the detection speed. Experiments demonstrate that our proposed method can achieve a detection speed of 123 frame per second (FPS) in GeForce GTX 1080 and can obtain an accuracy of 88.2% mean average precision (mAP)50:95. In the case of insufficient training samples, the method can still achieve an accuracy of 80.9% mAP50:95. In addition, we build a power meter images (PMIs) dataset, which contains 1800 images in real scene. The dataset and method we proposed can help with further upgrades of traditional substations. In the future, we also hope to extend the algorithm to edge computing cameras for substations. The newly developed dataset and code are available at https://github.com/zzfan3/electric_meter_detect_recognize.