# Real-Time Flying Object Detection with YOLOv8

arXiv: https://arxiv.org/abs/2305.09972 · HTML: https://mlsanity.com/paper/2305.09972/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Learning non-maximum suppression](https://mlsanity.com/paper/1705.02950/) (2017)

## Built on by (most cited first, 30 of 94)

- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing](https://mlsanity.com/paper/2404.09990/) (2024)
- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [A Survey for Foundation Models in Autonomous Driving](https://mlsanity.com/paper/2402.01105/) (2024)
- [UFO2: The Desktop AgentOS](https://mlsanity.com/paper/2504.14603/) (2025)
- [OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning](https://mlsanity.com/paper/2410.18963/) (2024)
- [GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities](https://mlsanity.com/paper/2412.04244/) (2024)
- [Learning from Massive Human Videos for Universal Humanoid Pose Control](https://mlsanity.com/paper/2412.14172/) (2024)
- [VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos](https://mlsanity.com/paper/2510.19488/) (2025)
- [A Survey on Quality Metrics for Text-to-Image Generation](https://mlsanity.com/paper/2403.11821/) (2024)
- [Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation](https://mlsanity.com/paper/2408.14819/) (2024)
- [HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative](https://mlsanity.com/paper/2403.02640/) (2024)
- [CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation](https://mlsanity.com/paper/2502.19842/) (2025)
- [FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers](https://mlsanity.com/paper/2507.12956/) (2025)
- [Match Stereo Videos via Bidirectional Alignment](https://mlsanity.com/paper/2409.20283/) (2024)
- [Towards Understanding Visual Grounding in Visual Language Models](https://mlsanity.com/paper/2509.10345/) (2025)
- [Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion](https://mlsanity.com/paper/2501.16679/) (2025)
- [QPT V2: Masked Image Modeling Advances Visual Scoring](https://mlsanity.com/paper/2407.16541/) (2024)
- [Prompt-Based Segmentation at Multiple Resolutions and Lighting Conditions using Segment Anything Model 2](https://mlsanity.com/paper/2408.06970/) (2024)
- [Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes](https://mlsanity.com/paper/2511.06457/) (2025)
- [UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation](https://mlsanity.com/paper/2411.19292/) (2024)
- [Advancing Human Action Recognition with Foundation Models trained on Unlabeled Public Videos](https://mlsanity.com/paper/2402.08875/) (2024)
- [ProgramAlly: Creating Custom Visual Access Programs via Multi-Modal End-User Programming](https://mlsanity.com/paper/2408.10499/) (2024)
- [ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving](https://mlsanity.com/paper/2504.03438/) (2025)
- [A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond](https://mlsanity.com/paper/2505.00737/) (2025)
- [YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications](https://mlsanity.com/paper/2512.05412/) (2025)
- [Move and Act: Enhanced Object Manipulation and Background Integrity for Image Editing](https://mlsanity.com/paper/2407.17847/) (2024)
- [HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection](https://mlsanity.com/paper/2501.05631/) (2025)
- [What's Making That Sound Right Now? Video-centric Audio-Visual Localization](https://mlsanity.com/paper/2507.04667/) (2025)
- [Visual Grounding Methods for Efficient Interaction with Desktop Graphical User Interfaces](https://mlsanity.com/paper/2407.01558/) (2024)

## Abstract (arXiv)

This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that achieves state-of-the-art results for flying object detection. We achieve this by training our first (generalized) model on a data set containing 40 different classes of flying objects, forcing the model to extract abstract feature representations. We then perform transfer learning with these learned parameters on a data set more representative of real world environments (i.e. higher frequency of occlusion, very small spatial sizes, rotations, etc.) to generate our refined model. Object detection of flying objects remains challenging due to large variances of object spatial sizes/aspect ratios, rate of speed, occlusion, and clustered backgrounds. To address some of the presented challenges while simultaneously maximizing performance, we utilize the current state-of-the-art single-shot detector, YOLOv8, in an attempt to find the best trade-off between inference speed and mean average precision (mAP). While YOLOv8 is being regarded as the new state-of-the-art, an official paper has not been released as of yet. Thus, we provide an in-depth explanation of the new architecture and functionality that YOLOv8 has adapted. Our final generalized model achieves a mAP50 of 79.2%, mAP50-95 of 68.5%, and an average inference speed of 50 frames per second (fps) on 1080p videos. Our final refined model maintains this inference speed and achieves an improved mAP50 of 99.1% and mAP50-95 of 83.5%