10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection
Journal:
arXiv
Published Date:
Mar 4, 2025
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.
