FFE-DETR: Frequency-Aware Feature Enhancement for Object Detection in Low-Light Scenarios

Object detection is a core task in computer vision, yet its performance is severely degraded in low-light environments, where foreground objects blend into the background, feature contrast is reduced, and object boundaries become blurred, ultimately impairing detection accuracy. To address this problem, we propose FFE-DETR, an end-to-end detection framework specifically designed for low-light scenes. The model incorporates a Frequency-Aware Feature Enhancer that applies Laplacian pyramid decomposition to separate low-frequency and high-frequency components. The low-frequency features are globally modeled to enhance foreground saliency and emphasize object boundaries, and the enhanced representation subsequently guides high-frequency detail restoration and noise suppression, yielding clearer and more discriminative features. In addition, a Multi-Scale Adaptive Feature Fusion module is introduced to efficiently integrate shallow texture information with deep semantic cues, enhancing the feature representation capability across different scales. Experimental results on widely used low-light benchmarks demonstrate that FFE-DETR consistently outperforms state-of-the-art methods and achieves significantly superior detection accuracy, highlighting its effectiveness and robustness.