基于全卷积网络的复杂背景红外弱小目标检测研究

Research on infrared small and weak target detection in complex background based on full convolutional networks

针对复杂背景红外弱小目标检测过程中存在的检测误差率高,检测时间过长等问题,提出基于全卷积网络的复杂背景红外弱小目标检测方法。分析复杂背景红外弱小目标检测的研究进展,找出不同方法的缺陷,采集红外图像,提取目标检测特征,并采用全卷积网络设计弱小目标检测的分类器,实现复杂背景红外弱小目标检测。实验结果表明,该方法的复杂背景红外弱小目标检测精度超过97%,具有较高的实际应用价值。

In order to solve the problems of high detection error rate and long detection time in the detection process of complex background infrared dim dim targets,a detection method of complex background infrared dim dim targets based on full convolutional network is proposed.This paper analyzes the research progress of complex back-ground infrared dim small targets detection,finds out the defects of different methods,collects infrared images,ex-tracts target detection features,and uses the full convolutional network to design a classifier for dim small targets detec-tion,and realizes the detection of complex background infrared dim small targets.The experimental results show that the accuracy of this method is more than 97%,and it has high practical application value.