Work

KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection

Year: 2025

Type: article

Abstract: Spaceborne infrared imagery provides substantial data support for ship detection due to its adaptability and all-weather reconnaissance capabilities. However, in complex maritime environments, its inadequate semantic information and feature distinctiveness increase the risk of false alarms and missed detections. To address these challenges, we propose a knowledge-driven feature fusion detection transformer (KDFF-DETR) for spaceborne infrared ship detection. KDFF-DETR presents an enhancement of RT-DETR. Specifically, we design a hybrid Fourier contextual (HFC) encoder to model the adequate semantic representation of high-level features through frequency-domain features and masked supervision. We further develop a visual knowledge-supervised feature reconstruction (VKFR) module, which introduces feature operators to represent visual knowledge and reconstruct handcrafted features to enhance their distinctiveness. Besides, a contribution-balanced feature fusion (CBFF) method for query selection is proposed, dynamically adjusting the weight distribution of handcrafted and deep features during different training stages to improve feature fusion accuracy and generalization ability. Experimental results indicate that KDFF-DETR surpasses state-of-the-art (SOTA) methods in various public datasets.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3586955

Authors: Yifei Peng, Ze Tao, Jian Zhang, Rongchang Zhao, Chao Liu, Hui Sun

Institutions: Central South University, PLA Academy of Military Science

Language: English

Topic: Infrared Target Detection Methodologies

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,800

Funder: National Natural Science Foundation of China