EFLNet: Enhancing Feature Learning for Infrared Small Target Detection

TL;DR

EFLNet is a novel deep learning framework that improves infrared small target detection by addressing class imbalance, bounding box sensitivity, and semantic layer importance, outperforming existing methods.

Contribution

The paper introduces EFLNet with an adaptive threshold focal loss, normalized Gaussian Wasserstein distance, and dynamic head mechanism for enhanced infrared small target detection.

Findings

Achieves superior detection performance over state-of-the-art methods.

Effectively handles class imbalance and bounding box sensitivity issues.

Demonstrates robustness and accuracy on specialized datasets.

Abstract

Single-frame infrared small target detection is considered to be a challenging task, due to the extreme imbalance between target and background, bounding box regression is extremely sensitive to infrared small target, and target information is easy to lose in the high-level semantic layer. In this article, we propose an enhancing feature learning network (EFLNet) to address these problems. First, we notice that there is an extremely imbalance between the target and the background in the infrared image, which makes the model pay more attention to the background features rather than target features. To address this problem, we propose a new adaptive threshold focal loss (ATFL) function that decouples the target and the background, and utilizes the adaptive mechanism to adjust the loss weight to force the model to allocate more attention to target features. Second, we introduce the normalized Gaussian Wasserstein distance (NWD) to alleviate the difficulty of convergence caused by the extreme sensitivity of the bounding box regression to infrared small target. Finally, we incorporate a dynamic head mechanism into the network to enable adaptive learning of the relative importance of each semantic layer. Experimental results demonstrate our method can achieve better performance in the detection performance of infrared small target compared to the state-of-the-art (SOTA) deep-learning-based methods. The source codes and bounding box annotated datasets are available at https://github.com/YangBo0411/infrared-small-target.