High-voltage Transmission Line Inspection Based on Multi-scale Lightweight Convolution
The use of unmanned aerial vehicles equipped with lightweight models for high-voltage power transmission line inspections is crucial for ensuring the safe operation of power transmission systems.Currently,insufficient feature extrac-tion in lightweight models may lead to inferior effectiveness in high-voltage power transmission line inspections,thus this paper proposes a multi-scale lightweight convolution(MSLConv).MSLConv fuses features at different scales and depths while achieving network lightweight.In the shuffle attention(SA)module,a novel and more adaptive channel shuffling method is introduced.The MSLConv is combined with SA to design two feature extraction modules,MSLConvSAl and MSLConvSA2,thereby enhancing the channel utilization of MSLConv.When processing sample output,this paper pro-poses a combined method based on sample-based dual normalization and activation.This method allows for finer adjustments of channel features in lightweight models with fewer channels and a better establishment of feature relation-ships between samples.Finally,this paper conducts a series of improvements based on the YOLOv5n baseline,resulting in two models:MSV1 and MSV2.The experimental results demonstrate that,compared to the baseline,MSV1 and MSV2 can reduce parameter quantities by 43.8％and 28.6％,and reduce computational loads by 38.1％and 23.8％,respectively.Additionally,mAP@.5(average detection accuracy value at an IoU threshold of 0.5)is improved by 5.2％and 5.4％,and mAP@.5:.95(average detection accuracy value when the IoU threshold is 0.5～0.95 and the step size is 0.05)is improved by 3.3％and 4.7％for MSV1 and MSV2,respectively.
high-voltage transmission line inspectionlightweight modelshuffle attentionMSLConvYOLOv5n
万方数据
维普
