SCR-Guided Difficulty-Aware Optimization for Infrared Small Target Detection

2026-06-17

AI Summary

This work addresses the challenges of infrared small target detection, which include strong background clutter, low contrast, and weak spatial response, rendering conventional geometry-based overlap metrics inadequate for accurate performance evaluation. To overcome this limitation, the authors propose the REEM framework, which—by introducing signal-to-clutter ratio (SCR) as a physically grounded visibility prior into the training process—modulates the soft-IoU learning signal in a difficulty-aware manner via differentiable SCR computation. This approach enhances the model’s focus on low-visibility targets without altering network architecture or increasing inference overhead. Evaluated on the U-Net-based MSHNet, the method demonstrates significant improvements in detection probability and IoU while substantially reducing false alarm rates, with particularly pronounced gains in low-SCR scenarios.

Abstract

Infrared small target detection remains challenging due to severe background clutter, low contrast, and weak spatial responses where geometric overlap alone is insufficient to characterize detection quality. In this work, we propose REEM (Reweighted Explicit-visibility Enhanced Modulation), a lightweight SCR-guided difficulty-aware optimization framework that incorporates Signal-to-Clutter Ratio (SCR) as a physically meaningful visibility prior during training. Instead of modifying the network architecture or directly optimizing SCR, REEM computes a ground-truth local SCR from the input image and applies a differentiable modulation to the soft-IoU learning signal, emphasizing low-visibility targets while preserving stable optimization and identical inference behavior. REEM is integrated into a U-Net-based MSHNet without introducing additional parameters, architectural modifications, or inference-time overhead. Extensive experiments demonstrate consistent improvements over the baseline, achieving higher IoU and detection probability (Pd) together with substantially reduced false alarms (FA), particularly under challenging low-visibility conditions. These results suggest that SCR-guided difficulty-aware optimization provides an effective and physically grounded complement to conventional overlap-based objectives for infrared small target detection. The code is available at https://github. com/yall-in-one/Reemm.

Problem

• infrared small target detection
• background clutter
• low contrast
• weak spatial response
• detection quality

Innovation

• SCR-guided optimization
• difficulty-aware learning
• infrared small target detection
• visibility prior
• soft-IoU modulation