# From Blurry to Brilliant Detection: YOLO-Based Aerial Object Detection with Super Resolution

Source: [From Blurry to Brilliant Detection: YOLO-Based Aerial Object Detection with Super Resolution](<https://www.sota2.com/research/paper/68fa79f41e209131cea803b7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T03:57:52.204Z

Authors: Ragib Amin Nihal, Benjamin Yen, Takeshi Ashizawa, Katsutoshi Itoyama, Kazuhiro Nakadai

Published: 2024-01-26T05:50:58.000Z

## Abstract

Aerial object detection presents challenges from small object sizes, high density clustering, and image quality degradation from distance and motion blur. These factors create an information bottleneck where limited pixel representation cannot encode sufficient discriminative features. B2BDet addresses this with a two-stage framework that applies domain-specific super-resolution during inference, followed by detection using an enhanced YOLOv5 architecture. Unlike training-time super-resolution approaches that enhance learned representations, our method recovers visual information from each input image. The approach combines aerial-optimized SRGAN fine-tuning with architectural innovations including an Efficient Attention Module (EAM) and Cross-Layer Feature Pyramid Network (CLFPN). Evaluation across four aerial datasets shows performance gains, with VisDrone achieving 52.5% mAP using only 27.7M parameters. Ablation studies show that super-resolution preprocessing contributes +2.6% mAP improvement while architectural enhancements add +2.9%, yielding +5.5% total improvement over baseline YOLOv5. The method achieves computational efficiency with 53.8% parameter reduction compared to recent approaches while achieving strong small object detection performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on VisDrone](<https://www.sota2.com/research/sota/object-detection-on-visdrone.md>): Object Detection; VisDrone; mAP50: 52.5.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.14661>)

[PDF](<https://arxiv.org/pdf/2401.14661.pdf>)