Efect detection technology for overhead transmission lines based on compressed images and YOLOv5 model
LIU Min
JIANG Liang
TIAN Yangyang
ZHANG Lu
CHEN Cen
Abstract：[Objective]Transmission lines are an important link in the transmission and use of electrical energy,and their safety and stability play a crucial role in the normal operation of the power system.Therefore,daily inspections of transmission lines are of great importance.Major accidents usually develop from small defects and hidden dangers.Daily inspections usually use manual,unmanned aerial vehicle,visualization channels,and other means.Regardless of the method,a large number of visualization,infrared,or ultraviolet photos need to be processed.However,due to the particularity of transmission lines,the installation conditions involve multiple environments,and the inspection image background is usually complex.Although the manual review method has high accuracy,it relies heavily on experience and has extremely low efficiency.Therefore,how to quickly and accurately identify inspection images of overhead transmission lines is the key to identifying defects in overhead transmission lines.The traditional image recognition method for transmission line inspection is prone to low defect recognition accuracy under complex background interference.[Methods]Therefore,to enhance the recognition accuracy of detection images of overhead transmission lines under complex backgrounds,a defect detection method that balances recognition efficiency and accuracy was proposed.The proposed method was based on compressed image technology combined with the YOLOv5 model.Firstly,an asymmetric feature aggregation compression algorithm based on sparse convolution was designed.The original image was encoded to reduce the space required by image storage data for storage and transmission.After being transmitted to the decryptor through the information channel,the compressed image was decoded and restored to improve the learning efficiency of local set features.At the same time,by the integration of the channel-spatial attention module(CSAM),the attention channel weight matrix and spatial weight matrix were obtained from the feature map,and the importance of the feature map region was determined through the weight matrix.In this way,the processing efficiency of the YOLOv5 model was improved.[Results]The compressed and restored image was input into the improved YOLOv5 model.The channel attention module(CAM)and the spatial attention module(SAM)were used to process the attention data on the channel and space of the image,respectively.The features of the target area were enhanced through global average pooling and maximum pooling,and the SAM was introduced to enhance the attention of channel attention to feature position information,so as to detect defective devices.The effectiveness of the proposed method was verified experimentally.[Conclusion]The inspection image data set of an overhead line was used as the basis for training and testing the proposed detection method.The results show that the sizes of the detection images are significantly reduced after compression using the proposed technique,and the sizes of the restored images are reduced by about 3 MB,compared to those of the original images,without distortion.The improved YOLOv5 model has high detection precision,with detection accuracy reaching 0.91 and detection time being as short as 0.87 s.The algorithm ensures detection accuracy while reducing image size and improving detection speed.
Keywords：overhead transmission linedefect detectionimage compressionimproved YOLOv5 modelasymmetric feature aggregation encoding and decoding networkchannel-spatial attention modulechannel-by-channel sparse residual convolutiondetection accuracy
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 152-159 )