Insulator Detection Method Using YOLO Combining Feature Reuse and Reconstruction
To overcome challenges such as low generalization performance and difficulty in identifying insulators amidst complex backgrounds in deep learning-based insulator defect detection methods,this study introduces a novel method based on the You Only Look Once(YOLO)with feature Reuse and Reconstruction(YOLO-RR)model,focusing on feature extraction and fusion.Firstly,in the feature extraction stage,a dense35 network is constructed based on DenseNet as the backbone network.By reusing features,the model enhances its perception of feature details,thereby improving detection accuracy under low saturation and low contrast imaging while reducing the number of network parameters.Secondly,in the feature fusion stage,an Hourglass-based Bidirectional Feature Pyramid Network(H-BiFPN)structure is introduced for bidirectional fusion of features at different scales.Through feature reconstruction and fusion,this method enriches feature information of varying scales,addressing the issue of information loss of small targets under continuous convolution and enhancing small target detection accuracy.Finally,the Wise Intersection of Union(WIoU)loss function is employed to optimize the model,enhancing prediction accuracy by focusing on common anchor boxes.Experimental results on the expanded Chinese Power Line Insulator Dataset(CPLID)demonstrate that the YOLO-RR model achieves a recognition rate of 93.6%with network parameters compressed to 5.16×106,outperforming comparative models.The proposed model meets the requirements of accurate localization and real-time performance for insulator defect detection,exhibiting robust detection performance even in scenarios with significant background interference and lighting effects.
国家科技期刊平台
NSTL
万方数据
