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 over distances, making the targets highly dim and prone to confusion with the background clutter, which makes the detector challenging to balance the precision and recall rate. 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 for improving 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 rate. Furthermore, the prosecution network utilizes a densely connected transformer structure, which can prevent small targets from disappearing in the network forward propagation. In addition, a fine-grained attention module is adopted to accurately locate the small targets. Experimental results show that CourtNet achieves the best F1-score on the two ISTD datasets, MFIRST (0.62) and SIRST (0.73).
翻译:红外线辐射在距离上衰减,使目标高度暗淡,容易与背景混乱混为一谈,从而使探测器难以平衡精确率和召回率。为了应对这一困难,本文件提议了一种基于神经网络的ISTD网络方法,称为CourtNet,它有三个子网络:起诉网络旨在提高召回率;被告网络致力于提高精确率;陪审团网络对其结果进行加权,以适应性地平衡精确率和召回率。此外,起诉网络使用一个紧密相连的变压器结构,可以防止小目标在网络前方传播中消失。此外,还采用了一个精细的注意模块,以准确定位小目标。实验结果表明,法院网络在ISTD的两个数据集MFIRST(0.62)和SIRST(0.73)上实现了最佳F1核心。