Single-Point Supervised High-Resolution Dynamic Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2408.01976v2
• Date: Thu, 8 Aug 2024 02:15:41 GMT
• Title: Single-Point Supervised High-Resolution Dynamic Network for Infrared Small Target Detection
• Authors: Jing Wu, Rixiang Ni, Feng Huang, Zhaobing Qiu, Liqiong Chen, Changhai Luo, Yunxiang Li, Youli Li,
• Abstract summary: We propose a single-point supervised high-resolution dynamic network (SSHD-Net) It achieves state-of-the-art (SOTA) detection performance using only single-point supervision. Experiments on the publicly available datasets NUDT-SIRST and IRSTD-1k demonstrate the effectiveness of our method.
• Score: 7.0456782736205685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small target detection (IRSTD) tasks are extremely challenging for two main reasons: 1) it is difficult to obtain accurate labelling information that is critical to existing methods, and 2) infrared (IR) small target information is easily lost in deep networks. To address these issues, we propose a single-point supervised high-resolution dynamic network (SSHD-Net). In contrast to existing methods, we achieve state-of-the-art (SOTA) detection performance using only single-point supervision. Specifically, we first design a high-resolution cross-feature extraction module (HCEM), that achieves bi-directional feature interaction through stepped feature cascade channels (SFCC). It balances network depth and feature resolution to maintain deep IR small-target information. Secondly, the effective integration of global and local features is achieved through the dynamic coordinate fusion module (DCFM), which enhances the anti-interference ability in complex backgrounds. In addition, we introduce the high-resolution multilevel residual module (HMRM) to enhance the semantic information extraction capability. Finally, we design the adaptive target localization detection head (ATLDH) to improve detection accuracy. Experiments on the publicly available datasets NUDT-SIRST and IRSTD-1k demonstrate the effectiveness of our method. Compared to other SOTA methods, our method can achieve better detection performance with only a single point of supervision.

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