基于轻量级YOLO-v4模型的变电站数字仪表检测识别

In order to accurately recognize the readings of digital instruments in the actual scene of substations, intelligently control substation security, and promote its intelligent development, the digital instruments in the substation are taken as the research object, and in view of real-time and accuracy, a lightweight YOLO-v4 model is proposed for the detection and recognition of digital instruments. Firstly, the digital instrument images captured from the Ordos substation are expanded by using the Albumentations framework, thus building an effective digital instrument data set for detection and recognition. After that, an efficient channel attention (ECA)-based deep separable convolution block (ECA-bneck-m) is constructed with attention mechanism, and further a lightweight YOLO-v4 model is proposed to conduct comparative experiments on model size and performance. Finally, experiments comparing model size and performance are performed. The results show that, the storage size of the model can be compressed by about 5 times nearly without loss of detection accuracy, and the processing speed of model can be increased from 24.0 frame/s to 36.9 frame/s, indicating that the proposed model can meet the requirements of real-time detection and recognition in the actual substation.