Target Driven Adaptive Loss For Infrared Small Target Detection

TL;DR

This paper introduces a target driven adaptive loss function that improves infrared small target detection by focusing on local regions and adjusting for scale and contrast, outperforming existing methods.

Contribution

The paper proposes a novel TDA loss with patch-based and adaptive strategies to enhance IRSTD performance, addressing local detection and robustness issues.

Findings

TDA loss improves detection accuracy on three IRSTD datasets.

The method enhances focus on local regions around targets.

It performs better than existing loss functions in experiments.

Abstract

We propose a target driven adaptive (TDA) loss to enhance the performance of infrared small target detection (IRSTD). Prior works have used loss functions, such as binary cross-entropy loss and IoU loss, to train segmentation models for IRSTD. Minimizing these loss functions guides models to extract pixel-level features or global image context. However, they have two issues: improving detection performance for local regions around the targets and enhancing robustness to small scale and low local contrast. To address these issues, the proposed TDA loss introduces a patch-based mechanism, and an adaptive adjustment strategy to scale and local contrast. The proposed TDA loss leads the model to focus on local regions around the targets and pay particular attention to targets with smaller scales and lower local contrast. We evaluate the proposed method on three datasets for IRSTD. The results demonstrate that the proposed TDA loss achieves better detection performance than existing losses on these datasets.