Ground Target Recognition and Damage Assessment of Patrol Missiles Based on Multi-source Information Fusion
For the multiple patrol missiles to attack the high defense capacity targets,a mobile ground target detection and damage assessment method based on multi-source information fusion is proposed.The multi-source information fusion of infrared images and RGB images is carried out by using IoU determination.A novel two-stage tightly coupled damage assessment method based on YOLO-VGGNet of patrol missiles to mobile ground targets is proposed.This method can fully use the advantage of deep semantic information extraction of CNNs and introduce the infrared damaging information simultaneously to achieve the online and real-time damage assessment of mobile ground targets.The results of simulation experiments show that the target recognition algorithm based on multi-source information fusion significantly improves the detection of mobile ground targets of patrol missiles.Compared with the traditional image-change-detection-based method and the two-CNN learning-based method,the real-time and online damage level assessment method based on YOLO-VGGNet improves the accuracy by 19% and 10.3%,respectively.
multi-source information fusiondamage assessmentconvolutional neural networksYOLO-VGGNetonline real-time assessment
万方数据
维普
