Infrared Remote Sensing Small Target Detection Method Based on Improved YOLOv4 Network
Targeting at the poor performance of traditional detection methods for infrared small target,a transferring learning and improved YOLOv4 network based infrared small target detection system is proposed.Firstly,the shallow features extracted by backbone of YOLOv4 network are enhanced,and the difficulty of infrared small target detection is reduced with combination of shallow features and deep features.Secondly,an attention mechanism is introduced to the detection head of YOLOv4 network to help the network focus on infra-red small targets of the feature maps,thus,the background interference to small target detection is reduced.Finally,the transferring learn-ing method is introduced to the training process of YOLOv4 network to solve the problem of lack of labeled training data for infrared small target detection.Experimental results based on public infrared small target detection dataset show that the proposed system improves the detection performance of YOLOv4 network for infrared small target,it also outperforms the other compared detection models.
deep learninginfrared remote sensingtarget detectiontransferring learningdeep neural networkone stage detection model
国家科技期刊平台
NSTL
万方数据
