基于D-DADA算法与DBE-YOLO网络的电表异常检测方法OA

Currently,the maintenance and anomaly detection of user-side smart meters primarily rely on professionals visiting the site,leading to low inspection efficiency,significant periodic testing burdens,and dependence on manual experience.A dataset of abnormal electricity meter images is created based on the inspection images obtained from a power grid company.This paper introduces a novel anomaly detection method for electricity meters that utilizes Diversity-Driven Differentiable Automatic Data Augmentation(D-DADA)algorithm and the Dual-Branch Feature Enhancement YOLO(DBE-YOLO)network to address issues such as complex backgrounds,varying target sizes,and obscured wiring in meter images.First,the DBE-YOLO model is designed to enhance the extraction of global contextual information and multiscale features by introducing cascaded dilated convolutions.It also employs a dual-branch aggregation network to overcome the limitations of the original model,including a restricted receptive field and fixed convolutional feature capture patterns.Second,the D-DADA algorithm is introduced,featuring a search strategy with diversity constraints to enhance the automatic discovery of a wider array of data augmentation strategies.This enables the model to learn the detection target features and patterns under various scenarios,angles,and lighting conditions,addressing the issue of insufficient model recognition performance owing to large intraclass variations.The experimental results indicate that the improved YOLOv8 model achieves an average detection accuracy of 79.6％across eight types of electricity meter anomalies,representing a 3.4 percentage point increase compared with the previous version.