Substation anomaly detection method based on attention mechanism and feature fusion

To ensure the safe, stable, and reliable operation of substations and improve the intelligent anomaly monitoring ability, this paper proposes an algorithm for detecting abnormal behaviors and equipment defects of substations based on an attention mechanism and feature fusion network. This algorithm designs the feature fusion structure of BiMFPN. It constructs multiple green channels of shallow input to deep output feature fusion, thus fusing a more extensive range of practical features without increasing the cost. At the same time, lightweight CBAM is added to the fusion network to effectively combine the extracted spatial location features with the rich semantic information in the image. In addition, the algorithm also adds a new detection head, which effectively improves the detection performance of multi-scale targets and is more sensitive to small targets. After experimental tests, the mAP and F1 scores of the algorithm are 69.3%and 71.7%, respectively, which are 9% and 8% higher than that of YOLOv5m. The experimental results show that this method can effectively detect abnormal behavior and faulty equipment around the substation and improve the intelligent detection level of the substation.