Obstacle Detection Around Aircraft on Ramps and Taxiways Through the Use of Computer Vision

Gauci, Jason; Zammit-Mangion, David

doi:10.2514/6.2009-5866

Cited by 13 publications

(13 citation statements)

References 10 publications

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“…While there is evidence of supervised and self-supervised techniques supported by computer vision for obstacle detection in self-driving cars [5]- [7] and while there is evidence of anomaly detection capabilities of autoencoders [8] with techniques including radar scans as inputs [9], obstacle detection for aircraft ground movement at aerodromes is still limited to supervised computer vision applications [10] and geometric camera angles [11]. This research therefore contributes to the state of the art by exploring the use of a self-supervised system using both computer vision and mmRadar data to detect anomalies by the use of simple auto encoders.…”

Section: Literature Reviewmentioning

confidence: 99%

Self-supervised Obstacle Detection During Autonomous UAS Taxi Operations

Shaikh

Petrunin

Zolotas

2023

AIAA SCITECH 2023 Forum

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This research explores the application of self-supervised learning techniques for obstacle detection and collision avoidance during UAS auto-taxi. Autoencoders were used to detect obstacles as anomalies by comparison of reconstruction errors. RGB cameras and millimetre wave radars covering conflict free zones (CFZs) around the own-ship were chosen to provide inputs to autoencoders. Results demonstrated that autoencoders were able to detect obstacles as anomalies within the CFZs but with certain limitations at lay the foundations of further work and investigation within the research area.

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Section: Literature Reviewmentioning

confidence: 99%

Self-supervised Obstacle Detection During Autonomous UAS Taxi Operations

Shaikh

Petrunin

Zolotas

2023

AIAA SCITECH 2023 Forum

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“…In particular, light detection and ranging (lidar) techniques are researched and used in practical applications such as forestry, oceanography, autonomous driving, and precision measurements [11][12][13][14] . To do so, various lidar methods have been developed including coherent lidars that use amplitude, phase, or frequency-modulated continuous-wave (CW) light [15][16][17] and pulsed lidars that operate by direct light detection of laser pulse propagation for time-of-flight measurements [18][19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Machine-learning-enhanced, phase-based multi-tone continuous-wave lidar

Bayer¹,

Demirel²,

Atalar³

et al. 2023

Photonic Instrumentation Engineering X

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Lidar technologies have been investigated and commercialized for various applications such as autonomous driving and aerial vehicles. The pulsed time of flight and frequency-modulated continuous-wave lidars are the two common lidar technologies that dominate. As an alternative to the available lidars, we developed the phase-based multi-tone continuouswave (PB-MTCW) technology that can perform single-shot simultaneous ranging and velocimetry measurements with a high resolution at distances far beyond the coherence length of a CW laser, without employing any form of sweeping. The proposed technique utilizes relative phase accumulations at phase-locked RF sidebands to identify the range of the target after a heterodyne detection of the beating of the echo signal with an unmodulated CW optical local oscillator (LO). Upto-date, we demonstrated that the PB-MTCW lidar could perform ranging ×500 beyond the coherence length of the laser with <1cm precision. Here, we implement machine learning (ML) algorithms to the PB-MTCW architecture to improve the ranging resolution, as well as to provide a solution to multi-target reflections using tone-amplitude variations. We used four different training schemes by utilizing the acquired RF tones and phases from simulation results, experimental results, and their combinations in a convolutional neural network model. We demonstrate that the ML algorithm yields an average mean square error of ~0.3mm compared to the actual target distance, hence enhancing the ranging resolution of PB-MTCW lidar. It is also shown that the ML algorithm can distinguish multiple targets in the same line of sight with a 98%±0.7% success rate depending on the targets' reflectance and distances.

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“…Soomok Lee et al [21] used the YOLO algorithm to detect objects on the apron, and by recognizing the intent of the aircraft to solve the potential problems that may arise from the autopilot of vehicles on the apron. Also, Gauci et al [22] provided a new idea for aircraft collision avoidance on the apron by installing cameras on the wingtip.…”

Section: Introductionmentioning

confidence: 99%

Research on conflict detection model for taxi‐in process on the apron based on aircraft wingtip keypoint detection

Zhang

Zhu

et al. 2022

IET Intelligent Trans Sys

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Apron safety is critical to aviation operations and aprons are prone to wingtip scraping accidents. However, few studies have been conducted on wingtip conflicts. In this paper, a class of aircraft conflict detection models based on wingtip keypoint detection is devised to solve the problem of wingtip scraping that may occur during the aircraft taxi‐in process on the apron. Firstly, a concise camera calibration technique is designed for the conversion between the pixel coordinate and the airport actual coordinate, which allows for the acquisition of high accuracy activity posture within the apron. Secondly, the static and dynamic safety protection zone delineation methods for wingtip conflict detection are given, taking the apron standard operating procedures and aircraft kinematic model into consideration. Then, the corresponding conflict detection models and algorithms are designed for the possible scraping between the vehicle and aircraft wingtips and between the aircraft and aircraft wingtips during the aircraft taxi‐in process on the apron. Finally, HRNet is selected to detect the keypoint of the wingtip by comparison, and the corresponding models and algorithm validation are carried out based on the apron physical sandbox. The verification results indicate that the models have strong accuracy and real‐time performance.

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Obstacle Detection Around Aircraft on Ramps and Taxiways Through the Use of Computer Vision

Cited by 13 publications

References 10 publications

Self-supervised Obstacle Detection During Autonomous UAS Taxi Operations

Self-supervised Obstacle Detection During Autonomous UAS Taxi Operations

Machine-learning-enhanced, phase-based multi-tone continuous-wave lidar

Research on conflict detection model for taxi‐in process on the apron based on aircraft wingtip keypoint detection

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