Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features

TL;DR

This paper introduces HintU, a novel network that leverages prior knowledge to recover lost local features in UNet-based models, significantly improving infrared small target detection accuracy.

Contribution

It proposes the Hint mechanism and enhancements to UNet architectures to better preserve local features for small target detection in infrared images.

Findings

HintU improves detection accuracy on three datasets.

It adds minimal computational cost (1.88 ms).

Enhances generalization of UNet-based methods.

Abstract

Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images. We propose HintU, a novel network to recover the local features lost by various UNet-based methods for effective infrared small target detection. HintU has two key contributions. First, it introduces the "Hint" mechanism for the first time, i.e., leveraging the prior knowledge of target locations to highlight critical local features. Second, it improves the mainstream UNet-based architecture to preserve target pixels even after downsampling. HintU can shift the focus of various networks (e.g., vanilla UNet, UNet++, UIUNet, MiM+, and HCFNet) from the irrelevant background pixels to a more restricted area from the beginning. Experimental results on three datasets NUDT-SIRST, SIRSTv2 and IRSTD1K demonstrate that HintU enhances the performance of existing methods with only an additional 1.88 ms cost (on RTX Titan). Additionally, the explicit constraints of HintU enhance the generalization ability of UNet-based methods. Code is available at https://github.com/Wuzhou-Quan/HintU.