Abstract
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. Codes will be available soon.
Abstract (translated)
红外小物体检测(ISOS)的目标是在红外图像中只包含几像素的杂乱背景中识别小物体。这是一个具有巨大挑战的任务,因为:1) 小物体缺乏足够的强度、形状和纹理信息;2) 小物体很容易在检测模型(如深度学习网络)通过连续降采样获得高级别语义特征和图像级响应面的过程中丢失。本文提出了一个可靠的ISOS检测模型,称为UCFNet,它能够处理这两个问题。它基于中心差分卷积(CDC)和快速傅里叶卷积(FFC)。一方面,CDC能够有效地指导网络学习小物体和背景之间的对比信息,因为对比信息在人类视觉系统中处理ISOS任务非常重要。另一方面,FFC能够获得图像级响应面并提取全局信息,同时防止小物体被淹没。在多个公共数据集上的实验表明,我们的方法 significantly outperforms 现有的ISOS模型,并可以为设计更好的ISOS深度模型提供有用的指导。代码将很快可用。
URL
https://arxiv.org/abs/2301.12093
