LH-DETR: A Lightweight Hybrid Architecture for End-To-End Object Detection in UAV Images

Abstract

Object detection in unmanned aerial vehicle (UAV) has become a research highlight at the intersection of computer vision and robotics technology, and its applications in security inspection, agricultural monitoring, disaster relief and others are becoming increasingly widespread. The key to achieving autonomous perception and decision-making of UAV lies in precise and real-time object detection. However, objects from the perspective of UAV often have characteristics such as small scale and dense distribution, coupled with limited onboard computing resources, which poses significant challenges to traditional detection algorithms. To address the trade-offs, this paper proposes LH-DETR, a lightweight hybrid architecture for end-to-end object detection, referring to three specialized innovations. We first put in the Wavelet-Mamba Hybrid Block (WMHB), a novel backbone component that synergistically combines the linear-complexity of Mamba state-space model for capturing long-range dependencies with the multi-scale feature extraction capabilities of wavelet transforms. To better identify small objects, a Frequency-Aware Dynamic FFN (FAD-FFN) is designed to selectively amplify critical high-frequency components—like edges and textures—by analyzing features in the frequency domain. Additionally, AutoSliding Varifocal Loss (ASVLoss) is defined to stabilize the model's optimization, which is an adaptive loss function that dynamically shifts its focus from medium-quality to high-quality predictions as training progresses. Experiments on public aerial datasets demonstrate that LH-DETR achieves an outstanding balance between accuracy and speed, significantly improving detection performance for small objects while greatly reducing the computational complexity.