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

Abstract

Infrared small target detection plays an important role in many military and civilian applications. Despite the great advances made by infrared small target detection 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 infrared small target detection research. To address the abovementioned issues, in this paper, we propose a robust infrared small target detection 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 infrared small target detection benchmark to advance the research in this field, named the NCHU-Seg dataset. To the best of our 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.).