To address the challenges of multi-scale differences,complex background interference,and unstable small target positioning in visual inspection of power towers,the existing methods still face issues such as insufficie...To address the challenges of multi-scale differences,complex background interference,and unstable small target positioning in visual inspection of power towers,the existing methods still face issues such as insufficient feature interaction and unstable confidence estimation,which lead to performance degradation in complex backgrounds and occlusion conditions.This paper proposes a precise inspection method for key power tower components using autonomous drone positioning.To this end,this paper makes three key improvements to the you only look once version 11(YOLOv11)framework.First,it constructs C3k2-adaptive multi-receptive field block(C3k2-AMRB),combining multiple dilated convolutions with a reparameterization mechanism to significantly expand the receptive field and enhance multi-scale feature extraction.Second,it designs a hierarchical wavelet interaction unit(HWIU),which leverages high-and low-frequency decomposition and reconstruction of wavelet transform(WT)to achieve cross-scale semantic alignment,enhancing feature discriminability in complex backgrounds.Third,it proposes a distribution-aware confidence refinement head(DACR-Head),which adaptively calibrates classification confidence based on the statistical characteristics of the predicted bounding-box corner distribution,improving the localization stability and accuracy of small targets.Experiments on the inspection of power line assets dataset(InsPLAD)dataset show that the integrated approach achieves a component detection accuracy at intersection over union(IoU)=0.5(CDA_(50))of 88.3%and a component detection robustness(CDR_(50:95))of 69.8%,respectively,improvements of 4.4%and 7.0%over the baseline.展开更多
The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution...The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.展开更多
基金supported by the National Natural Science Foundation of China(No.61702347)Hebei Academy of Sciences Basic Research Operating Fund Project(No.2025PF21)。
文摘To address the challenges of multi-scale differences,complex background interference,and unstable small target positioning in visual inspection of power towers,the existing methods still face issues such as insufficient feature interaction and unstable confidence estimation,which lead to performance degradation in complex backgrounds and occlusion conditions.This paper proposes a precise inspection method for key power tower components using autonomous drone positioning.To this end,this paper makes three key improvements to the you only look once version 11(YOLOv11)framework.First,it constructs C3k2-adaptive multi-receptive field block(C3k2-AMRB),combining multiple dilated convolutions with a reparameterization mechanism to significantly expand the receptive field and enhance multi-scale feature extraction.Second,it designs a hierarchical wavelet interaction unit(HWIU),which leverages high-and low-frequency decomposition and reconstruction of wavelet transform(WT)to achieve cross-scale semantic alignment,enhancing feature discriminability in complex backgrounds.Third,it proposes a distribution-aware confidence refinement head(DACR-Head),which adaptively calibrates classification confidence based on the statistical characteristics of the predicted bounding-box corner distribution,improving the localization stability and accuracy of small targets.Experiments on the inspection of power line assets dataset(InsPLAD)dataset show that the integrated approach achieves a component detection accuracy at intersection over union(IoU)=0.5(CDA_(50))of 88.3%and a component detection robustness(CDR_(50:95))of 69.8%,respectively,improvements of 4.4%and 7.0%over the baseline.
基金supported by the National Natural Science Foundation of China under Grants 62362040,61662033supported by the Science and Technology Project of the State Grid Jiangxi Electric Power Co.,Ltd.of China under Grant 521820210006.
文摘The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.
