AFE-Net: Attention-Guided Feature Enhancement Network for Infrared Small Target Detection

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 4208 - 4221
• Main Authors: Wang, Keyan, Wu, Xueyan, Zhou, Peicheng, Chen, Zuntian, Zhang, Rui, Yang, Liyun, Li, Yunsong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attention mechanism; Background noise; Clutter; convolutional neural network (CNN); Decoding; Detection; False alarms; Feature extraction; Infrared imagery; infrared small target detection; Modules; nonlocal dependency; Object detection; Semantics; Signal to noise ratio; Sparse matrices; Target detection; Task analysis
• DOI: 10.1109/JSTARS.2024.3354244
• ISSN: 1939-1404

Infrared small target detection is considerably challenging due to the few pixels in targets, low signal-to-noise ratio, and complex background. In this article, we propose an effective attention-guided feature enhancement network (AFE-Net), which can leverage the local and nonlocal features of targets and background in infrared images. The AFE-Net consists of three key modules, namely encoder and decoder interactive guidance (EDIG) module, cascading false alarm removal (CFAR) module, and random scale input (RSI) module. Specifically, in the EDIG module, we employ a CA mechanism on encoding and decoding layers to select feature channels with higher contribution. Then, we impose a bottom-up pointwise attention block to highlight the features of small infrared targets and suppress possible noise by incorporating the low-level detailed features into the high-level semantic features. The CFAR module extracts affluent global features by cascading nonlocal operations of different layers, which can remove clutters with similar features to infrared targets. The RSI module is placed in front of the entire detection network to extract multiscale features of infrared small targets, which can enhance the robustness of the proposed network. Experimental results on the SIRST dataset and comprehensive comparisons with representative methods demonstrate the superiority of our proposed method.