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LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection by Yu, Chuang; Liu, Yunpeng; Zhao, Jinmiao; Shi, Zelin is a scholarly article available to read on EtoBox.

What is LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection about?

Limited by equipment limitations and the lack of target intrinsic features, existing infrared small target detection methods have difficulty meeting actual comprehensive performance requirements. Therefore, we propose an innovative lightweight and robust network (LR-Net), which abandons the complex structure and achieves an effective balance between detection accuracy and resource consumption. Specifically, to ensure the lightweight and robustness, on the one hand, we construct a lightweight feature extraction attention (LFEA) module, which can fully extract target features and strengthen information interaction across channels. On the other hand, we construct a simple refined feature transfer (RFT) module. Compared with direct cross-layer connections, the RFT module can improve the network's feature refinement extraction capability with little resource consumption. Meanwhile, to solve the problem of small target loss in high-level feature maps, on the one hand, we propose a low-level feature distribution (LFD) strategy to use low-level features to supplement the information of high-level features. On the other hand, we introduce an efficient simplified bilinear interpolation atten

Author

Yu, Chuang; Liu, Yunpeng; Zhao, Jinmiao; Shi, Zelin

Published

2024

Language

EN