Infrared small object detection (ISOS) aims to segment small objects only covered with several pixels from clutter background in infrared images. It's of great challenge due to: 1) small objects lack of sufficient intensity, shape and texture information; 2) small objects are easily lost in the process where detection models, say deep neural networks, obtain high-level semantic features and image-level receptive fields through successive downsampling. This paper proposes a reliable detection model for ISOS, dubbed UCFNet, which can handle well the two issues. It builds upon central difference convolution (CDC) and fast Fourier convolution (FFC). On one hand, CDC can effectively guide the network to learn the contrast information between small objects and the background, as the contrast information is very essential in human visual system dealing with the ISOS task. On the other hand, FFC can gain image-level receptive fields and extract global information while preventing small objects from being overwhelmed.Experiments on several public datasets demonstrate that our method significantly outperforms the state-of-the-art ISOS models, and can provide useful guidelines for designing better ISOS deep models. Code are available at https://github.com/wcyjerry/BasicISOS.
翻译:红外小天体探测(ISOS) 旨在将小天体分割为红红外图像中来自杂乱背景的几像素所覆盖的小像素。 它具有巨大的挑战性,因为:(1) 小天体缺乏足够的强度、形状和质感信息;(2) 小天体在探测模型(比如深神经网络、获得高层次的语义特征和图像接收场)过程中很容易丢失。 另一方面, FFFC可以获得图像级的可接收字段和提取全球信息,同时防止小天体被挤压。 在几个公共数据集上所作的介绍表明,我们的方法大大超越了ISOS模型(CDC)和快速Fourier Convolution(FFC)的状态。 一方面, CDC可以有效地指导网络学习小天体和背景之间的对比信息,因为对比信息在与ISOS任务打交道的人类视觉系统中非常重要。 另一方面, FFCFC可以获取图像级可接收的可接收字段,并提取全球信息,同时防止小天体被压过。 在几个公共数据集上所作的介绍表明,我们的方法大大超越了State-art Stat-art ISOS ISOS) 模型, 并且可以提供更好的设计 ISO/ ISO/ basir s remabre slims slims moxy smalmal modemal modely smals smal smal dess 。</s>