Thermal infrared image semantic segmentation with Iightweight edge assisted context guided network

Thermal power image segmentation is the key step of power equipment infrared diagnosis. Power equipment infrared diagnosis can timely and accurately diagnose the potential accidents and fault precursors of operating power equipment, which is an important part of power inspection. With the increasing popularity of mobile intelligent terminals, modern power inspection requires deploying semantic segmentation models on mobile devices. Considering the small memory capacity of mobile devices, we propose a new lightweight network architecture, called Edge-Assisted Context Guided Network (ECGNet), for semantic segmentation of thermal infrared electrical equipment images. ECGNet has been carefully designed to learn the context information of thermal infrared images and improve the problem of edge blur on the premise of small parameters and small memory consumption. Under the same number of parameters, a large number of experiments on LS-ETS dataset show that ECGNet can obtain better results than the most advanced method.