# Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

arXiv: https://arxiv.org/abs/2003.09085 · HTML: https://mlsanity.com/paper/2003.09085/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 30 of 35)

- [DiffusionSat: A Generative Foundation Model for Satellite Imagery](https://mlsanity.com/paper/2312.03606/) (2023)
- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection](https://mlsanity.com/paper/2208.08738/) (2022)
- [SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery](https://mlsanity.com/paper/2209.13351/) (2022)
- [Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection](https://mlsanity.com/paper/2304.08876/) (2023)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Focus-and-Detect: A Small Object Detection Framework for Aerial Images](https://mlsanity.com/paper/2203.12976/) (2022)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [Visible and Clear: Finding Tiny Objects in Difference Map](https://mlsanity.com/paper/2405.11276/) (2024)
- [Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future](https://mlsanity.com/paper/2410.05577/) (2024)
- [YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion](https://mlsanity.com/paper/2410.17144/) (2024)
- [Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects](https://mlsanity.com/paper/2309.10588/) (2023)
- [SelvaBox: A high-resolution dataset for tropical tree crown detection](https://mlsanity.com/paper/2507.00170/) (2025)
- [A systematic study of the foreground-background imbalance problem in deep learning for object detection](https://mlsanity.com/paper/2306.16539/) (2023)
- [Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime](https://mlsanity.com/paper/2307.09562/) (2023)
- [Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning](https://mlsanity.com/paper/2412.11582/) (2024)
- [MAC: ModAlity Calibration for Object Detection](https://mlsanity.com/paper/2310.09461/) (2023)
- [A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings](https://mlsanity.com/paper/2312.13620/) (2023)
- [ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation](https://mlsanity.com/paper/2103.09699/) (2021)
- [Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes](https://mlsanity.com/paper/2509.01332/) (2025)
- [Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors](https://mlsanity.com/paper/2208.04363/) (2022)
- [Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery](https://mlsanity.com/paper/2209.02564/) (2022)
- [Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/2402.00153/) (2024)
- [Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection](https://mlsanity.com/paper/2504.20602/) (2025)
- [CEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects](https://mlsanity.com/paper/2506.09897/) (2025)
- [Controllable Reference Guided Diffusion with Local Global Fusion for Real World Remote Sensing Image Super Resolution](https://mlsanity.com/paper/2506.23801/) (2025)
- [RS-TinyNet: Stage-wise Feature Fusion Network for Detecting Tiny Objects in Remote Sensing Images](https://mlsanity.com/paper/2507.13120/) (2025)
- [Local Information Matters: A Rethink of Crowd Counting](https://mlsanity.com/paper/2508.16970/) (2025)

## Abstract (arXiv)

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.