Machine Vision for UAS Ground Operations

Coombes, Matthew; Eaton, William; Chen, Wenhua

doi:10.1007/s10846-017-0542-5

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…The integration of additional sensors, like LiDAR and camera sen-sors, also improves object detection and classification, thereby facilitating safer operations of autonomous baggage tractors on airport ramps [6]. However, adding extra sensors to flying vehicles could increase weight, power consumption, and certification challenges, which is why Coombes et al [7] advocated for a machine vision-only approach as the most practical solution for enabling automated taxiing. Their solution was based on semantic segmentation combined with a Bayesian Network classifier using a single monocular camera, an instrument that nearly all vehicles possess, thereby representing a streamlined and efficient way to reach the direct sensing capabilities necessary for autonomous navigation.…”

Section: Airport Autonomous Navigationmentioning

confidence: 99%

Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks

de Frutos Carro,

Villalonga,

Cruz

2024

Aerospace

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Autonomous navigation within airport environments presents significant challenges, mostly due to the scarcity of accessible and labeled data for training autonomous systems. This study introduces an innovative approach to assess the performance of vision-based models trained on synthetic datasets, with the goal of determining whether simulated data can train and validate navigation operations in complex airport environments. The methodology includes a comparative analysis employing image processing techniques and object detection algorithms. A comparative analysis of two different datasets was conducted: a synthetic dataset that mirrors real airport scenarios, generated using the Microsoft Flight Simulator 2020®video game, and a real-world dataset. The results indicate that models trained on a combination of both real and synthetic images perform much better in terms of adaptability and accuracy compared to those trained only on one type of dataset. This analysis makes a significant contribution to the field of autonomous airport navigation and offers a cost-effective and practical solution to overcome the challenges of dataset acquisition and algorithm validation. It is thus believed that this study lays the groundwork for future advancements in the field.

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Section: Airport Autonomous Navigationmentioning

confidence: 99%

Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks

de Frutos Carro,

Villalonga,

Cruz

2024

Aerospace

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“…In [12] the attitude, altitude, and motion of a UAV can be estimated using a camera mounted on the UAV, employing either catadioptric or fish-eye sensors. The machine vision component of a system devised to enable Unmanned Aerial Systems (UAS) to autonomously maneuver around civil aerodromes, relying solely on a monocular camera, is expounded upon [13].…”

Section: Introductionmentioning

confidence: 99%

Power Supply Technologies for Drones and Machine Vision Applications: A Comparative Analysis and Future Trends

Pekias

Maraslidis

Tsipouras

et al. 2023

Telecom

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The field of Unmanned Aerial Vehicles (UAVs), or drones, is encountering quick development in the areas of air transportation and computerization. Progress in innovation has prompted more noteworthy capacities and highlights in UAVs, which are currently broadly involved by the military and flying industry for an assortment of high-end generally safe errands. Highly advanced UAVs that can be controlled remotely via a controller, mobile phone, or ground station cockpit have been developed through the integration of automation technology and machine vision, which includes thermal imaging, cameras, sensors, and other sensors. The three primary characteristics of UAVs will be investigated in this study, namely power-source technology, deep-learning neural networks for computer vision, and some of the applications that are used the most. The goal is to thoroughly examine these characteristics and offer suggestions for addressing some of the difficulties of optimizing UAV performance and also exploring potential future trends.

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“…With the continuous development of machine vision, with its advantages of high efficiency, accuracy, and stability, it has become a very good method for noncontact measurement [9][10][11][12]. rough image processing algorithm, the parameter detection of target in image is completed, in which Hough transform detects target in image through space conversion and achieves better results.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

Zhao

Ding

Cao

et al. 2021

Journal of Advanced Transportation

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The machining accuracy of the compensation hole of the automobile brake master cylinder directly determines the safety of the automobile and the reliability of parking. How to detect the parameters of the compensation hole with high precision becomes a crucial issue. In this paper, by analyzing the principle of Hough transform detection technology and several optimization algorithms, a new method combining Zernike moment and improved gradient Hough transform is proposed to detect the circular hole parameters. The simulation experiment shows that the proposed algorithm satisfies 0.1 pixels in the coordinate detection of the center position, and the radius detection accuracy is 0.05 pixels, with fast detection speed and good robustness. Compared with the random Hough transform algorithm and the gradient Hough transform algorithm, the algorithm proposed in this paper has higher detection accuracy, faster detection speed, and better robustness, which meets the online detection accuracy requirements of the brake master cylinder compensation hole.

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Machine Vision for UAS Ground Operations

Cited by 12 publications

References 20 publications

Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks

Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks

Power Supply Technologies for Drones and Machine Vision Applications: A Comparative Analysis and Future Trends

A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

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