Research On Multi-Source Image Fusion Target Detection Technology Based on Neural Network

Abstract

Target detection and recognition technology based on multi-sensor fusion such as radar, visible light, and infrared has become a development trend in industries and military fields. Deep learning networks have strong characterization learning capabilities and are good at extracting various complex features from multi-source heterogeneous data. This article reviews the research and development status of multi-source image fusion and target detection at home and abroad, and discusses the multi-source information fusion technology and multi-sensor system construction plan. Aiming at the fusion target detection problem of visible light image and infrared image, a target detection algorithm based on decision-level fusion is introduced. The YOLOv3 network is used to detect the visible light image and the infrared image separately, and then achieve rapid target detection based on decision-level fusion.