Effective Receptive Field Ordering Matters for Infrared Small Target Detection

2026-07-27

AI Summary

This work addresses a critical oversight in existing infrared small target detection methods—their neglect of how the effective receptive field (ERF) is organized and its impact on feature refinement dynamics. The study pioneers modeling deep feature transformation as a progressive residual correction process and introduces a novel ERF scheduling theory grounded in scale–frequency correspondence and nonlinear non-commutativity. To hierarchically order ERFs, the authors devise a multi-grid-inspired V-cycle strategy. The resulting Receptive Field Ordering Network (RFONet), built exclusively with standard 3×3 convolutions, achieves state-of-the-art performance across multiple benchmarks with only 1.16M parameters and over 157 FPS, while demonstrating exceptional noise robustness and cross-dataset generalization. This establishes ERF organization as a distinct architectural dimension beyond conventional receptive field design.

Abstract

In this work, we investigate a previously unexplored architectural dimension for infrared small target detection: the organization of effective receptive fields (ERFs) during feature refinement. Unlike existing approaches that primarily improve individual feature operators, we argue that ERF organization constitutes an architectural dimension independent of receptive field design itself, and formulate deep feature transformation as a progressive residual correction process, from which a theoretical framework for ERF scheduling is established. Specifically, we reveal that ERF refinement is governed by two fundamental properties: scale-frequency correspondence, which aligns different ERF scales with distinct residual frequency characteristics, and nonlinear non-commutativity, which makes different ERF orderings produce fundamentally different refinement trajectories. Together, these properties show that ERF organization, rather than ERF scale alone, governs refinement dynamics. Guided by these principles, we propose Receptive Field Ordering Network (RFONet), which realizes hierarchical ERF scheduling through a multigrid-inspired V-cycle strategy using only standard $3\times3$ convolutions. RFONet achieves state-of-the-art performance on multiple benchmarks with only 1.16M parameters and over 157 FPS inference speed. Beyond empirical performance, our theoretical analysis provides theoretical guarantees for stable residual refinement under perturbations, frequency shifts, and partial occlusions, which are consistently reflected in superior noise robustness and cross-dataset generalization. Finally, our framework reformulates ERF organization as a task-dependent optimization objective, providing a principled foundation for future adaptive receptive field scheduling.

Problem

• Infrared Small Target Detection
• Effective Receptive Field
• Feature Refinement
• Receptive Field Organization
• Residual Correction

Innovation

• Effective Receptive Field
• Infrared Small Target Detection
• Receptive Field Ordering
• Residual Refinement
• V-cycle Scheduling