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

Abstract

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for onorbit 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 learningbased 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.TABLE OF CONTENTS 1. INTRODUCTION .

Jensen–Shannon Divergence You Only Look Once: A Real‐Time Robotic Grasp Detection Network

“…The method mainly addressed the localization and recognition problems of objects using industrial robots with a specific wooden block target. In addition, YOLOv5 is also widely used in agricultural crop detection, [ 32,33 ] satellite component detection, [ 34 ] remote sensing image detection, [ 35,36 ] and so on. YOLOv5 has been validated to achieve good detection results and real‐time detection performance in these applications.…”

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

“…Faster R-CNN is renowned for its high detection accuracy and employs a two-stage deep learning framework. This network structure impacts computational efficiency and speed, which are crucial factors for real-time applications [ 69 ]. The YOLO model, on the other hand, is a one-stage object section approach that is known for significant speed and real-time performance.…”

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

“…These techniques use deep learning models to analyze the digital image or video frames and identify the objects of interest. For example, in [227], Faster R-CNN and YOLOv5 has been used for relative navigation task in On-Orbit Servicing and Active Space Debris Removal Technology. They showed that in a formation flight simulation, while Faster R-CNN is more accurate than YOLOv5 but YOLO is 10 times faster.…”