Lossless Compression and Optimization Method of Data Flow Efficiency of Infrared Image Depth Learning Model of Substation Equipment

Abstract

Infrared detection of substation equipment has become one of the important means for live detection of power grid equipment. The insulation fault and equipment defect of electrical equipment in operation can be found by using infrared thermal imaging technology. Infrared image deep learning model usually needs a very large computational cost and storage space, which greatly limits the application of deep learning model in embedded terminal. In order to apply the deep learning model well in embedded devices with limited resources, this paper proposes a lossless compression method of infrared image deep learning model of substation equipment, which can reduce the size of the deep learning network model by 35 to 49 times without loss of recognition accuracy. A hybrid sparse matrix storage format based on recursion and a cache blocking method based on multi-core/many-core processors are proposed to optimize the data flow efficiency between processors.