CourtNet: Dynamically balance the precision and recall rates in infrared small target detection

Abstract

Infrared small-target detection (ISTD) is an important computer vision task. ISTD aims at separating small targets from complex background clutter. The infrared radiation decays with distance, making the targets highly dim and prone to confusion with the background clutter, which makes the detector challenging to balance the precision and recall rates. To deal with this difficulty, this paper proposes a neural-network-based ISTD method called CourtNet, which has three sub-networks: the prosecution network is designed to improve the recall rate; the defendant network is devoted to increasing the precision rate; the jury network weights their results to adaptively balance the precision and recall rates. CourtNet takes the structure of Transformers, whose feature resolution remains unchanged. Furthermore, the prosecution network utilizes a densely connected structure, which can prevent small targets from disappearing in the forward propagation. In addition, a fine-grained attention module performs attention inside patches to accurately locate the small targets. This paper implements extensive experiments on two ISTD datasets, MFIRST and SIRST, and compares CourtNet with ten other traditional and deep-learning-based methods. Experimental results show that with the fast detection speed (60.61 FPS), CourtNet achieves the best F1 score, 0.62 (in MFIRST) and 0.73 (in SIRST), among the compared methods. The code and dataset will be available at https://github.com/PengJingchao/CourtNet.