Infrared dim and small target detection based on two-stage U-skip context aggregation network with a missed-detection-and-false-alarm combination loss

Abstract

Infrared small target detection (ISTD) is a critical technique in both civil and military applications such as leak and defect inspection, cell segmentation for medicine analysis, early-warning systems and so on. Over the last decade, numerous ISTD methods have been proposed, such as methods based on image denoising, visual saliency detection, low-rank matrix recovery and traditional machine learning, but training an end-to-end deep model to detect small targets has not been fully investigated. In this regard, the paper proposes a novel deep model called UCAN for ISTD which concatenates two context aggregation networks and connects them using U-skip connections. A Missed-detection-and-False-alarm Combination(MFC) loss function, which is based on the Neyman-Pearson decision theory, is proposed to train the model and can well balance the detection rate and the false alarm rate. In addition, a two-stage detection scheme which involves a cascade of two UCANs is proposed to further improve the overall detection performance of ISTD. Extensive experiments on real infrared sequences and a single-frame image set and the comparison with state-of-the-art methods demonstrate the superiority of the proposed model.