An automatic power line inspection method based on an improved SegNet network

Abstract

UAVs （unmanned aerial vehicles） are now involved in intelligent transmission line inspection. Given the complex image backgrounds captured by UAVs and poor line inspection accuracy and low detection speed of UAVs， the paper proposes a power line inspection algorithm based on an improved SegNet model. Firstly， residual modules and asymmetric convolutions are introduced into the encoder to reduce the computational burden on the network. Secondly， the network layers of the decoding layer are reduced， and the features of the encoder and decoder are fused to improve inspection accuracy. Finally， the improved SegNet algorithm is used to train the power line dataset. The accuracy and mean intersection over union reach up to 89.4% and 86.62% respectively， and the single detection time is 46 ms. The experimental results show that the algorithm based on the improved SegNet model can achieve high-precision and real-time power line detection.

Keywords

• uav inspection
• deep learning
• improved segnet
• residual module
• asymmetric convolution