A Robust Infrared Small Target Detection Method Jointing Multiple Information and Noise Prediction: Algorithm and Benchmark

Siqiang Meng, Congxuan Zhang, Qi Shi, Zhen Chen, Weiming Hu, Feng Lu

Abstract: Infrared small target detection (IRSTD) plays an important role in many military and civilian applications. Despite the great advances made by IRSTD studies in recent years, most of the existing methods have difficulty in balancing detection probabilities and false alarms. Moreover, there are only a few public datasets for infrared small targets, which limits the development of IRSTD research. To address the abovementioned issues, in this article, we propose a robust IRSTD method that joins multiple pieces of information and noise predictions, named MINP-Net. Specifically, we first design a gradient and contextual information extraction module to extract multiscale features from an input infrared image. Second, we construct a noise prediction network to model the background noise. Third, we plan a regional positioning branch to provide a coarse target location to decrease the false alarm ratio. In addition, we build a new IRSTD benchmark to advance the research in this field, named the NCHU-Seg dataset. To the best of the authors’ knowledge, the NCHU-Seg dataset is the largest real-world scene dataset for evaluating infrared small target segmentation methods. For a comprehensive evaluation, we compare our method with some of the state-of-the-art methods on both the well-known NUAA-SIRST dataset and our NCHU-Seg dataset. The experimental results demonstrate that the proposed MINP-Net method performs better in terms of detection effectiveness and segmentation accuracy and effectively balances the detection probabilities and false alarms with complex backgrounds. (The code and dataset are available at https://github.com/PCwenyue.)