Helicopter flying obstacle detection based on the fusion of infrared and optical images

Abstract

Helicopters often encounter obstacles such as towers and high-voltage lines when flying at low altitude, and the safety problem is increasingly prominent. Optical images have high resolution, which provide rich color, texture, edge and other details of the detection object. Infrared images can still maintain the advantage of high detection rate at night or in the environment with poor visibility. Combining the characteristics and advantages of infrared and optical images, this paper designs a dual branch convolution neural network to detect helicopter flying obstacles. For infrared images, a single branch infrared image feature extraction network SBI-Net (Single Branch Infrared image Network) is designed to automatically extract the features of infrared images; For optical images, a single branch optical image feature extraction network SBO-Net (Single Branch Optical image Network) is designed to extract the features of optical images; Finally, the two networks are fused, and a dual branch feature fusion network IODBFF-Net (Dual Branch Feature Fusion Network model based on Infrared and Optical image) is proposed. The experimental results show that compared with infrared single branch network and optical single branch network, the detection accuracy of dual branch convolution neural network is improved by 2.06% and 40.25% respectively.