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ILNet: Low-level Matters for Salient Infrared Small Target Detection by Li, Haoqing; Yang, Jinfu; Wang, Runshi; Xu, Yifei is a scholarly article available to read on EtoBox.

What is ILNet: Low-level Matters for Salient Infrared Small Target Detection about?

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22%

Author

Li, Haoqing; Yang, Jinfu; Wang, Runshi; Xu, Yifei

Published

2023

Language

EN