Defect Detection on Power Lines Based on Edge Technology and Deep Network
The traditional transmission line detection is easy to be affected by foggy weather,and the detection accuracy and efficiency are low.In this paper,a high-precision and high-efficiency detection method is proposed,which integrates multi-module de-fog network,network partitioning strategy and PowerNet network model.Firstly,based on the end-to-end learning,a multi-module defogging network is designed to solve the problem of low detection accuracy of transmission line defects caused by fog.Then,in order to improve the inspection efficiency of edge technology,a network partitioning strategy based on binary particle swarm is designed.On the basis of these,PowerNet network model is proposed to solve the problem of low accuracy of transmission line defect detection.Finally,the method proposed in this paper is validated and analyzed by experiments.The experimental results show that the proposed method has high accuracy and real-time defect detection,and its accuracy and efficiency can reach 99.3%and 28 ms photo,respectively.It can be seen that the method proposed in this paper has high engineering practical value.
power transmission linedefect detectionimage defoggingnetwork partitiondetection accuracy
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