HER-DETR: Lightweight UAV Object Detection Based on High-Frequency Enhancement and Multi-Scale Sparse Fusion

The rapid growth of the low-altitude economy has accelerated the deployment of unmanned aerial vehicle (UAV) technology in a wide range of applications, including power infrastructure inspection and disaster relief operations. These scenarios impose stringent real-time processing demands on onboard vision systems. However, aerial images are characterized by a high density of small and distant targets, which significantly weakens feature representation quality, and current state-of-the-art detection models carry substantial computational overhead that conflicts with the limited hardware resources available on embedded UAV platforms. This paper addresses both challenges simultaneously by proposing a novel lightweight, end-to-end detection framework designated the High-frequency Enhancement Retentive Detection Transformer (HER-DETR). The framework introduces three coordinated technical contributions. First, a lightweight backbone network termed the Cross Stage Partial High-frequency Enhancement Network is constructed. This backbone integrates a cross-stage high-frequency residual enhancement mechanism to alleviate feature attenuation commonly observed in small-target scenarios. Second, a Depthwise Intrascale High-Low Feature Interaction block is designed for intra-scale feature interaction at the highest semantic level, strengthening fine-grained representation through a dual-frequency decoupling strategy. Third, a Retention Feature Fusion module is developed as the core cross-layer feature fusion component, enabling bidirectional multi-scale feature integration via a lightweight sparse attention mechanism. Comprehensive experiments on the VisDrone2019 and HIT-UAV benchmark datasets confirm that HER-DETR achieves state-of-the-art performance. Compared with the RT-DETR-r18 baseline, the proposed framework improves mean average precision at a 0.50 intersection-over-union threshold by 2.0 percentage points on VisDrone2019 and by 1.6 percentage points on HIT-UAV, while simultaneously reducing model parameter count and computational floating-point operations by 41.6% and 35.3%, respectively. These results validate the framework’s capability to balance detection accuracy and computational efficiency for real-time UAV deployment.