# An Efficient Aerial Image Detection with Variable Receptive Fields

arXiv: https://arxiv.org/abs/2504.15165 · HTML: https://mlsanity.com/paper/2504.15165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Efficient Multi-Scale Attention Module with Cross-Spatial Learning](https://mlsanity.com/paper/2305.13563/) (2023)
- [UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery](https://mlsanity.com/paper/2501.01855/) (2025)
- [HIC-YOLOv5: Improved YOLOv5 For Small Object Detection](https://mlsanity.com/paper/2309.16393/) (2023)

## Built on by (most cited first, 2 of 2)

- [UAV-DETR: DETR for Anti-Drone Target Detection](https://mlsanity.com/paper/2603.22841/) (2026)
- [CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling](https://mlsanity.com/paper/2607.05177/) (2026)

## Abstract (arXiv)

Aerial object detection using unmanned aerial vehicles (UAVs) faces critical challenges including sub-10px targets, dense occlusions, and stringent computational constraints. Existing detectors struggle to balance accuracy and efficiency due to rigid receptive fields and redundant architectures. To address these limitations, we propose Variable Receptive Field DETR (VRF-DETR), a transformer-based detector incorporating three key components: 1) Multi-Scale Context Fusion (MSCF) module that dynamically recalibrates features through adaptive spatial attention and gated multi-scale fusion, 2) Gated Convolution (GConv) layer enabling parameter-efficient local-context modeling via depthwise separable operations and dynamic gating, and 3) Gated Multi-scale Fusion (GMCF) Bottleneck that hierarchically disentangles occluded objects through cascaded global-local interactions. Experiments on VisDrone2019 demonstrate VRF-DETR achieves 51.4% mAPtextsubscript50 and 31.8% mAPtextsubscript50:95 with only 13.5M parameters. This work establishes a new efficiency-accuracy Pareto frontier for UAV-based detection tasks.