FusionNet:Detection of Foreign Objects in Transmission Lines During Inclement Weather

To address the low accuracy and moderate speed of traditional foreign object detection in transmission line image inspection, the FusionNet is proposed based foreign object detection algorithm in severe weather. First, the FB module(Fusion Block) is proposed in this algorithm, combined with the CA attention(Coordinate Attention) mechanism and the Hardswish activation function, so that the network can increase the learning of the location information based on the attention on the overall classification information, and reduce the network parameters. Then, based on FasterNet, the FSB module (Fusion Speed Block)is proposed to extract the space more efficiently. In addition, the FMB module(Fusion Memory Block) is proposed, which makes full use of the accumulated information in the past to extract more accurate and abundant features. Finally, the utilization of EfficiCIoU as a loss function serves to expedite model convergence and enhance detection precision. Experimental results show that on the dataset in this paper, the improved algorithm mAP@0.5 reaches 98.27%, the model parameters are reduced by 130.42M compared with the Faster-RCNN model, and the accuracy is improved by 19.62% and 4.63% compared with SSD and YOLOv7 models, respectively. The performance on the China Power Line Insulator Dataset (CPLID) is also excellent, reaching 99.17% mAP@0.5, an improvement of 1.85% compared with the baseline model. Compared to the existing models, the FusionNet model is smaller in size and has higher detection accuracy. It can accurately detect targets in inclement weather and perform the task of foreign body detection in transmission lines.