Infrared Small Object Detection Using Deep Interactive U-Net

Abstract

Infrared objects acquired from a long-distance have small sizes and are easily submerged by a complex and variable background. The existing deep network detection framework suffers greatly from the feature spatial resolution loss caused by the networks’ depth and multiple downsampling operations, which is extremely detrimental for small object detection. So, a crucial and urgent goal is, how to trade-off network depth and feature spatial resolution, while learning feature context representation and interaction to distinguish from the background. To this end, we propose a deep interactive U-Net architecture (short for DI-U-Net) with high feature learning and feature interaction ability. First, feature learning is first achieved through a multi-level and high-resolution network structure. This structure ensures feature resolution as the network depth increase, and also focus on the object’s global context information. Then, the feature interactive is further achieved by the dense feature encoder (DFI) module to learn object local context information. The proposed method yields strong object context representation and well discriminability, as well as a good fit for infrared small object detection. Extensive experiments are conducted on the SISRT dataset and Synthetic dataset, demonstrating the superiority and effectiveness of the proposed deeper U-Net compared to previous state-of-the-art detection methods.