Detection and Identification of Transmission Line Damage Prevention Behavior Based on Improved YOLOv5s
It is crucial for the safety of the power system in the entire energy transmission process.Aiming at the main external force destruction behavior of super large engineering vehicles and fireworks in the transmission line,the single-stage target detection algorithm YOOv5s is improved.Firstly,for the working environment of the transmission line with heavy rain,fog and dust,the re-stricted contrast limited adaptive histogram equalization(CLAHE)algorithm is introduced to defog the image,and improve the image contrast;In response to the long distance problem of detecting targets,a coordinate attention(CA)mechanism is added to the YOLOv5s network to enhance the model's ability to locate targets;The nearest neighbor difference sampling method in the original network is replaced with the lightweight universal up-sampling operator content-aware reassembly of features(CARAFE),which bet-ter captures the feature map while introducing smaller parameter quantities;Finally,in the feature fusion layer of the network,a ghost-shuffle convolution(GSConv)module with channel shuffling idea is used to replace the standard convolution module,reducing the model parameters,and then Slim_Neck feature fusion structure is utilized to enhance the target attention,reducing the model parameters while improving the detection accuracy.The experimental results show that the mean average precision(mAP)of the im-proved YOLOv5s network improves by 4.4％,the number of the parameters reduces by 3.4％,and the memory of the weight model by 2.7％,proving that the algorithm is effectiveness.
target detectionexternal force damageYOLOv5sCA attentionCARAFEGSConv_Slimneck
万方数据
维普
