LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2408.02780v1
• Date: Mon, 5 Aug 2024 18:57:33 GMT
• Title: LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection
• Authors: Chuang Yu, Yunpeng Liu, Jinmiao Zhao, Zelin Shi,
• Abstract summary: We propose an innovative lightweight and robust network (LR-Net) LR-Net abandons the complex structure and achieves an effective balance between detection accuracy and resource consumption. We achieve 3rd place in the "ICPR 2024 Resource-Limited Infrared Small Target Detection Challenge Track 2: Lightweight Infrared Small Target Detection"
• Score: 2.6617665093172445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 attention module (SBAM) to promote the guidance constraints of low-level features on high-level features and the fusion of the two. In addition, We abandon the traditional resizing method and adopt a new training and inference cropping strategy, which is more robust to datasets with multi-scale samples. Extensive experimental results show that our LR-Net achieves state-of-the-art (SOTA) performance. Notably, on the basis of the proposed LR-Net, we achieve 3rd place in the "ICPR 2024 Resource-Limited Infrared Small Target Detection Challenge Track 2: Lightweight Infrared Small Target Detection".

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