SSE-YOLOv5: a real-time fault line selection method based on lightweight modules and attention models

To address the problems of low precision and poor anti-noise performance of the standard route selection method for the small current grounding faults, a fault line selection approach based on YOLOv5 network that integrates attention modules and lightweight models is proposed. First, grounding system fault’s zero sequence current is utilized as the basis for fault discrimination. A wavelet transform is employed to translate the zero sequence current to a two-dimensional time–frequency map to create a dataset. However, due to the impact of the lack of training sets on the accuracy of line selection, we constructed a simulation model for small current grounding faults based on actual faults. By modifying the fault location, fault angle, and grounding resistance, we generated a simulation dataset to expand the training set. Second, to reduce the impact of noise on fault features during line selection, the SE channel attention model is used to fuse it into the backbone of the YOLOv5 detection network, significantly improving the network's accuracy in detecting fault areas. Finally, to achieve high line selection accuracy and good real-time performance in the detection network, the lightweight network model ShuffleNetV2 is introduced into the constructed network. ShuffleNetV2 reduces the number of network model parameters through its deep separable convolution, improving the real-time performance of line selection. The proposed algorithm in this study was compared with four other algorithms to verify its advantages. The experimental results reveal that the proposed method reached a line selection accuracy of 93.6% under the condition of a small amount of real data samples, while maintaining a line selection accuracy of over 90% in the presence of noise. When the image resolution is 640 × 640, its detection speed is 122fps, indicating good real-time performance.