Infrared target detection method using adaptive feature fusion

Abstract

As the key technology of low altitude airspace security and air attack, vision-based air target detection has better concealment and lower cost compared with radar, radio and other detection methods. Based on yolov3, this paper proposes an air infrared target detection algorithm using adaptive feature fusion, which can effectively improve the accuracy of air infrared multi-scale target detection. Firstly, the construction of air infrared target detection data set is completed, and the target characteristics in the data set are statistically analysed. Secondly, the output layer of backbone is adjusted, and the four times down sampling feature map is used to replace the eight times down sampling feature map to enhance the detection ability of small targets. Thirdly, aiming at the problem of weakening feature expression caused by cross-scale feature fusion in FPN, a pixel level feature adaptive fusion module is designed. Finally, two improved SPP modules are added to neck to enrich the expression ability of feature map through the fusion of local features and global features. Experimental results show that compared with yolov3, our algorithm improves the map from 86.53% to 90.26% while keeping the detection speed basically unchanged.

MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion

“…An attention fusion module was used to enhance the spatial localization and semantic information features of small infrared targets and improve the feature representation ability of the network. Zheng et al [23] proposed an airborne infrared target detection algorithm based on adaptive feature fusion based on the Yolov3 [24] algorithm, which improved the detection precision of multi-scale airborne infrared targets.…”