Gradient-Enhanced Feature Pyramid Network for Infrared Small Target Detection

Detecting infrared small targets from complex background is a challenging task. Due to the low signal-to-noise ratio and few pixels of targets, it is difficult to get accurate edge segmentation, and the targets are easily mixed up by adjacent region. To overcome these problems, we propose a gradient-enhanced feature pyramid network (GEFPN) in this letter. Specifically, we first generate gradient information under the assistance of supplementary gradient enhancement (SGE) branch, which is conductive to highlight gradient magnitude and mitigate the inaccurate edge location of small targets. On the basis of this, the proposed network utilizes a dilated cross-stage partial module (DCSPM) to refine the multiscale features and encode supplemental gradient information into the main FPN structure. Moreover, we construct a patch attention fusion module (PAFM), which fully collects both spatial details and semantic information. The experimental results show that the proposed GEFPN can achieve excellent detection performance with mean intersection over union (IoU) reaching 0.939 and 0.732 on public NUDT-SIRST and SIRST-Aug datasets, respectively, and with 0.42 M parameters and inference speed of 67.47 FPS. The code of GEFPN is available at: https://github.com/xiyunqiao/irst3.