Bird Nest Detection Of Transmission Line Inspection Image Based On Deep Learning

Birds will nest on some transmission towers,forming bird damage,which seriously threatens the safe operation of transmission lines.In order to solve the problem of bird damage,it is necessary to inspect the transmission line and detect the bird nest.The current transmission line inspection has entered the era of unmanned aerial vehicle inspection instead of manual inspection,and combines machine vision and deep learning technology to achieve automatic line inspection and detection.Aiming at the problems of slow speed,low accuracy and poor detection effect of multi-target and small target in the current transmission line inspection nest detection algorithm,this paper proposes a lightweight YOLOv3 transmission line nest detection method.The main contents are as follows:Firstly,aiming at the problem of poor image quality of transmission line bird nest inspection data set caused by weather,the image denoising and background weakening are carried out by bilateral smoothing filtering,and the image defogging is carried out by dark channel prior defogging method based on guided filtering to improve the image quality.Aiming at the problem of small amount of data and unable to build data sets,the data enhancement method based on C-DCGAN and Mosaic data enhancement method are proposed to enhance the data set.Then,in view of the slow speed and low detection accuracy of the current nest detection algorithm,this paper conducts in-depth research on the YOLOv3 algorithm,and designs the backbone network based on deep separable convolution.The weight file size decreases from 241.082MB to 45.374MB,and the FPS increases from 17.45 of the original model to 38.80.The feature fusion structure based on PANet was designed.After the official data set test,the accuracy was greatly improved,and the mAP value increased from 86.37%to 93.29%.Based on.the optimization of backbone network and feature fusion structure,this paper proposes a lightweight and high-precision bird nest detection algorithm based on deep separable convolution and PANet.Finally,in the experimental test stage,the optimization training strategy based on transfer learning and label smoothing is adopted to train the model on the transmission line bird nest inspection data set constructed in this paper.Compared with the original model,the performance verification of the proposed algorithm in terms of loss function,mAP accuracy index and F1 accuracy index is realized,and the effectiveness of the proposed algorithm improvement and its superiority over other algorithms are verified by item-by-item ablation experiments and comparative experiments with other algorithms.The experimental results show that the proposed transmission line nest detection algorithm can effectively detect the transmission line nest.In terms of detection accuracy and detection speed,it is superior to the similar algorithms.The average detection accuracy can reach 95%,and the FPS reaches 36.91 in the image with resolution of 416×416,which can be competent for the current stage of transmission line inspection image nest detection task.