You Only Look Omni Gradient Backpropagation for Moving Infrared Small Target Detection

TL;DR

This paper introduces BP-FPN, a novel backpropagation-driven feature pyramid network designed specifically for small moving infrared target detection, addressing key challenges in feature representation and achieving state-of-the-art results.

Contribution

It presents the first FPN designed entirely from a backpropagation perspective, incorporating GILS and DGR to improve small target feature learning.

Findings

BP-FPN achieves new state-of-the-art performance on multiple datasets.

The proposed method introduces negligible computational overhead.

Extensive experiments validate the effectiveness of the approach.

Abstract

Moving infrared small target detection is a key component of infrared search and tracking systems, yet it remains extremely challenging due to low signal-to-clutter ratios, severe target-background imbalance, and weak discriminative features. Existing deep learning methods primarily focus on spatio-temporal feature aggregation, but their gains are limited, revealing that the fundamental bottleneck lies in ambiguous per-frame feature representations rather than spatio-temporal modeling itself. Motivated by this insight, we propose BP-FPN, a backpropagation-driven feature pyramid architecture that fundamentally rethinks feature learning for small target. BP-FPN introduces Gradient-Isolated Low-Level Shortcut (GILS) to efficiently incorporate fine-grained target details without inducing shortcut learning, and Directional Gradient Regularization (DGR) to enforce hierarchical feature consistency during backpropagation. The design is theoretically grounded, introduces negligible computational overhead, and can be seamlessly integrated into existing frameworks. Extensive experiments on multiple public datasets show that BP-FPN consistently establishes new state-of-the-art performance. To the best of our knowledge, it is the first FPN designed for this task entirely from the backpropagation perspective.