Learning Dynamic Local Context Representations for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2412.17401v1
• Date: Mon, 23 Dec 2024 09:06:27 GMT
• Title: Learning Dynamic Local Context Representations for Infrared Small Target Detection
• Authors: Guoyi Zhang, Guangsheng Xu, Han Wang, Siyang Chen, Yunxiao Shan, Xiaohu Zhang,
• Abstract summary: Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. We propose LCRNet, a novel method that learns dynamic local context representations for ISTD. With only 1.65M parameters, LCRNet achieves state-of-the-art (SOTA) performance.
• Score: 5.897465234102489
• License:
• Abstract: Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. Effective detection relies on capturing local contextual information at the appropriate scale. However, small-kernel CNNs have limited receptive fields, leading to false alarms, while transformer models, with global receptive fields, often treat small targets as noise, resulting in miss-detections. Hybrid models struggle to bridge the semantic gap between CNNs and transformers, causing high complexity.To address these challenges, we propose LCRNet, a novel method that learns dynamic local context representations for ISTD. The model consists of three components: (1) C2FBlock, inspired by PDE solvers, for efficient small target information capture; (2) DLC-Attention, a large-kernel attention mechanism that dynamically builds context and reduces feature redundancy; and (3) HLKConv, a hierarchical convolution operator based on large-kernel decomposition that preserves sparsity and mitigates the drawbacks of dilated convolutions. Despite its simplicity, with only 1.65M parameters, LCRNet achieves state-of-the-art (SOTA) performance.Experiments on multiple datasets, comparing LCRNet with 33 SOTA methods, demonstrate its superior performance and efficiency.

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