A Nonlocal Enhanced Feature Pyramid Network for Infrared Small Target Detection

Infrared small target suffers from weak features and complex background. The existing detection methods are usually unable to effectively extract global context and maintain the features of infrared small targets. To overcome the problems, we design a nonlocal enhanced feature pyramid network (NLFPNet) based on an encoder–decoder framework by additionally global contextual features modeling. In the encoder stage, small target features are extracted by a ResNet-18-based backbone network. Then, global contextual information of small targets is exploited by using a nonlocal enhanced pyramid pooling module (NLPPM), which is conductive to estimate the correlation between pixels in a wide range and enhance the global prior information. In the decoder stage, we obtain multilevel feature representation through asymmetric attention fusion module (AAFM), which reasonably modulates and maintains the shallow-level spatial details and deep-level semantic information. The experimental results show that the proposed NLFPNet can achieve an improved detection performance with mean Intersection over Union (IoU) of 0.739 and probability of detection (Pd) of 0.953 on the public SIRST-Aug dataset. The code is available at
https://github.com/xiyunqiao/irst1
.