RepISD-Net: Learning Efficient Infrared Small-Target Detection Network via Structural Re-Parameterization

Infrared small-target detection is a challenging task for deep learning-based methods, because targets tend to disappear in the deep layers. To handle this problem, the existing deep neural networks usually apply various dense and skip connections for feature maintenance. Although these well-designed networks have achieved good detection performance, the complex network structures reduce their efficiency. In this article, we propose a simple yet efficient network (RepISD-Net) for infrared small-target detection. The core of our RepISD-Net is to use different network architectures but equivalent model parameters for training and inference, respectively. Specifically, in the training phase, we design a parallel multibranch edge compensation block (ECB) to enhance the local salient features and capture finer contour characteristic of infrared small targets. In the inference phase, the multibranch topology structures are merged into a single branch with only cascaded $3 \times 3$ convolutions for fast inference. We conduct extensive experiments on several public datasets to validate the effectiveness of our method. Experimental results demonstrate that our RepISD-Net can achieve comparable or even better detection performance with significant acceleration in inference speed as compared with state-of-the-art infrared small-target detection methods. Our code is available at https://github.com/dalinlin-Wu/RepISD-Net.