# Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets

arXiv: https://arxiv.org/abs/2301.09056 · HTML: https://mlsanity.com/paper/2301.09056/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 22 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [Satellite Pose Estimation Challenge: Dataset, Competition Design and Results](https://mlsanity.com/paper/1911.02050/) (2019)
- [A Spacecraft Dataset for Detection, Segmentation and Parts Recognition](https://mlsanity.com/paper/2106.08186/) (2021)

## Built on by (most cited first, 8 of 8)

- [Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting](https://mlsanity.com/paper/2401.02588/) (2024)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection](https://mlsanity.com/paper/2302.00824/) (2023)
- [SatSplatYOLO: 3D Gaussian Splatting-based Virtual Object Detection Ensembles for Satellite Feature Recognition](https://mlsanity.com/paper/2406.02533/) (2024)
- [Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations](https://mlsanity.com/paper/2311.01703/) (2023)
- [AI-Driven Collaborative Satellite Object Detection for Space Sustainability](https://mlsanity.com/paper/2508.00755/) (2025)
- [SpY: A Context-Based Approach to Spacecraft Component Detection](https://mlsanity.com/paper/2406.18709/) (2024)
- [A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection](https://mlsanity.com/paper/2511.00777/) (2025)

## Abstract (arXiv)

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.