Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. Recently, convolutional neural networks have achieved significant advantages in general object detection. With the development of Transformer, the scale of SIRST models is constantly increasing. Due to the limited training samples, performance has not been improved accordingly. The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets. To highlight this issue, we propose a negative sample augmentation method in this paper. Specifically, a negative augmentation approach is proposed to generate massive negatives for self-supervised learning. Firstly, we perform a sequential noise modeling technology to generate realistic infrared data. Secondly, we fuse the extracted noise with the original data to facilitate diversity and fidelity in the generated data. Lastly, we proposed a negative augmentation strategy to enrich diversity as well as maintain semantic invariance. The proposed algorithm produces a synthetic SIRST-5K dataset, which contains massive pseudo-data and corresponding labels. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed. Compared with other state-of-the-art (SOTA) methods, our method achieves outstanding performance in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection over union (IoU).

翻译：单帧红外小目标检测旨在从杂波背景中识别小型目标。近年来，卷积神经网络在通用目标检测领域展现出显著优势。随着Transformer的发展，SIRST模型的规模不断扩大，但受限于训练样本数量，其性能未能同步提升。红外数据集的质量、数量和多样性对小目标检测至关重要。针对这一问题，本文提出一种负样本增强方法。具体而言，我们提出基于负增强的自监督学习框架来生成海量负样本：首先，采用序贯噪声建模技术生成逼真红外数据；其次，将提取的噪声与原始数据融合以提升生成数据的多样性与保真度；最后，提出一种在增强多样性的同时保持语义不变性的负样本增强策略。本算法生成的合成SIRST-5K数据集包含海量伪数据及对应标签。凭借红外小目标数据的丰富多样性，本算法显著提升了模型性能与收敛速度。与当前最优方法相比，本方法在检测概率、虚警率和交并比指标上均取得优异表现。