EFSI-DETR: Efficient Frequency-Semantic Integration for Real-Time Small Object Detection in UAV Imagery

• URL: http://arxiv.org/abs/2601.18597v1
• Date: Mon, 26 Jan 2026 15:41:37 GMT
• Title: EFSI-DETR: Efficient Frequency-Semantic Integration for Real-Time Small Object Detection in UAV Imagery
• Authors: Yu Xia, Chang Liu, Tianqi Xiang, Zhigang Tu,
• Abstract summary: EFSI-DETR is a novel detection framework that integrates efficient semantic feature enhancement with dynamic frequency-spatial guidance.<n>Experiments on VisDrone and CODrone benchmarks demonstrate that our EFSI-DETR achieves the state-of-the-art performance with real-time efficiency.
• Score: 10.339425380819513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to limited feature representation and ineffective multi-scale fusion. Existing methods underutilize frequency information and rely on static convolutional operations, which constrain the capacity to obtain rich feature representations and hinder the effective exploitation of deep semantic features. To address these issues, we propose EFSI-DETR, a novel detection framework that integrates efficient semantic feature enhancement with dynamic frequency-spatial guidance. EFSI-DETR comprises two main components: (1) a Dynamic Frequency-Spatial Unified Synergy Network (DyFusNet) that jointly exploits frequency and spatial cues for robust multi-scale feature fusion, (2) an Efficient Semantic Feature Concentrator (ESFC) that enables deep semantic extraction with minimal computational cost. Furthermore, a Fine-grained Feature Retention (FFR) strategy is adopted to incorporate spatially rich shallow features during fusion to preserve fine-grained details, crucial for small object detection in UAV imagery. Extensive experiments on VisDrone and CODrone benchmarks demonstrate that our EFSI-DETR achieves the state-of-the-art performance with real-time efficiency, yielding improvement of \textbf{1.6}\% and \textbf{5.8}\% in AP and AP$_{s}$ on VisDrone, while obtaining \textbf{188} FPS inference speed on a single RTX 4090 GPU.

Related papers

• UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects [7.1136620172045]
  Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets.<n>This paper proposes an end-to-end object detection framework, UFO-DETR, which integrates an LSKNet-based backbone network to optimize the receptive field and reduce the number of parameters.
  arXiv  Detail & Related papers  (2026-02-26T07:37:45Z)
• FAIM: Frequency-Aware Interactive Mamba for Time Series Classification [87.84511960413715]
  Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition.<n>We propose FAIM, a lightweight Frequency-Aware Interactive Mamba model.<n>We show that FAIM consistently outperforms existing state-of-the-art (SOTA) methods, achieving a superior trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-11-26T08:36:33Z)
• FMC-DETR: Frequency-Decoupled Multi-Domain Coordination for Aerial-View Object Detection [18.023418423273082]
  We propose FMC-DETR, a novel framework with frequency-decoupled fusion for aerial-view object detection.<n>First, we introduce the Wavelet Kolmogorov-Arnold Transformer (WeKat) backbone, which applies cascaded wavelet transforms to enhance global low-frequency context perception.<n>Next, a lightweight Cross-stage Partial Fusion (CPF) module reduces redundancy and improves multi-scale feature interaction.<n>Finally, we introduce the Multi-Domain Feature Coordination (MDFC) module, which unifies spatial, frequency, and structural priors to balance detail preservation and global enhancement.
  arXiv  Detail & Related papers  (2025-09-27T02:28:22Z)
• RT-DETR++ for UAV Object Detection [0.0]
  This paper introduces RT-DETR++, which enhances the encoder component of the RT-DETR model.<n>We introduce a channel-gated attention-based upsampling/downsampling mechanism.<n>Second, we incorporate CSP-PAC during feature fusion.
  arXiv  Detail & Related papers  (2025-09-11T05:33:52Z)
• High-Frequency Semantics and Geometric Priors for End-to-End Detection Transformers in Challenging UAV Imagery [6.902247657565531]
  We introduce HEDS-DETR, a holistically enhanced real-time Detection Transformer tailored for aerial scenes.<n>First, we propose a novel High-Frequency Enhanced Semantics Network (HFESNet) backbone, which yields highly discriminative features.<n>Second, our Efficient Small Object Pyramid (ESOP) counteracts information loss by efficiently fusing high-resolution features.<n>Third, we enhance decoder stability and localization precision with two synergistic components.
  arXiv  Detail & Related papers  (2025-07-01T14:56:56Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [92.4205087439928]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose the Self-supervised Transfer (PST) and the FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.<n>This combined approach enables FUSE to construct a universal image-event that only requires lightweight decoder adaptation for target datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Efficient Oriented Object Detection with Enhanced Small Object Recognition in Aerial Images [2.9138705529771123]
  We present a novel enhancement to the YOLOv8 model, tailored for oriented object detection tasks.<n>Our model features a wavelet transform-based C2f module for capturing associative features and an Adaptive Scale Feature Pyramid (ASFP) module that leverages P2 layer details.<n>Our approach provides a more efficient architectural design than DecoupleNet, which has 23.3M parameters, all while maintaining detection accuracy.
  arXiv  Detail & Related papers  (2024-12-17T05:45:48Z)
• Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints [51.83081671798784]
  Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability.<n>DiT's practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference.<n>We propose Skip-DiT, an image and video generative DiT variant enhanced with Long-Skip-Connections (LSCs) - the key efficiency component in U-Nets.
  arXiv  Detail & Related papers  (2024-11-26T17:28:10Z)
• CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion [138.40422469153145]
  We propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. We show that CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2022-11-26T02:40:28Z)
• Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes [82.4186966781934]
  We introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules. With negligible extra overhead, Ret3D achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-18T03:48:58Z)
• iffDetector: Inference-aware Feature Filtering for Object Detection [70.8678270164057]
  We introduce a generic Inference-aware Feature Filtering (IFF) module that can easily be combined with modern detectors. IFF performs closed-loop optimization by leveraging high-level semantics to enhance the convolutional features. IFF can be fused with CNN-based object detectors in a plug-and-play manner with negligible computational cost overhead.
  arXiv  Detail & Related papers  (2020-06-23T02:57:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.