10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection

• URL: http://arxiv.org/abs/2503.02662v2
• Date: Mon, 10 Mar 2025 12:26:22 GMT
• Title: 10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection
• Authors: Biqiao Xin, Qianchen Mao, Bingshu Wang, Jiangbin Zheng, Yong Zhao, C. L. Philip Chen,
• Abstract summary: Binarized neural networks (BNNs) are distinguished by their exceptional efficiency in model compression.<n>We propose the Binarized Infrared Small-Target Detection Network (BiisNet)<n>BiisNet preserves the core operations of binarized convolutions while integrating full-precision features into the network's information flow.
• Score: 48.074211420276605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread deployment of Infrared Small-Target Detection (IRSTD) algorithms on edge devices necessitates the exploration of model compression techniques. Binarized neural networks (BNNs) are distinguished by their exceptional efficiency in model compression. However, the small size of infrared targets introduces stringent precision requirements for the IRSTD task, while the inherent precision loss during binarization presents a significant challenge. To address this, we propose the Binarized Infrared Small-Target Detection Network (BiisNet), which preserves the core operations of binarized convolutions while integrating full-precision features into the network's information flow. Specifically, we propose the Dot Binary Convolution, which retains fine-grained semantic information in feature maps while still leveraging the binarized convolution operations. In addition, we introduce a smooth and adaptive Dynamic Softsign function, which provides more comprehensive and progressively finer gradient during backpropagation, enhancing model stability and promoting an optimal weight distribution. Experimental results demonstrate that BiisNet not only significantly outperforms other binary architectures but also has strong competitiveness among state-of-the-art full-precision models.

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