Browsing by Author "Barnes, Stuart"

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    Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach
    (Springer, 2025-05) Thomas, Julien; Kuang, Boyu; Wang, Yizhong; Barnes, Stuart; Jenkins, Karl
    The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception.
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    A dataset for autonomous aircraft refueling on the ground (AGR)
    (IEEE, 2023-09-01) Kuang, Boyu; Barnes, Stuart; Tang, Gilbert; Jenkins, Karl W.
    Automatic aircraft ground refueling (AAGR) can improve the safety, efficiency, and cost-effectiveness of aircraft ground refueling (AGR), a critical and frequent operation on almost all aircraft. Recent AAGR relies on machine vision, artificial intelligence, and robotics to implement automation. An essential step for automation is AGR scene recognition, which can support further component detection, tracking, process monitoring, and environmental awareness. As in many practical and commercial applications, aircraft refueling data is usually confidential, and no standardized workflow or definition is available. These are the prerequisites and critical challenges to deploying and benefitting advanced data-driven AGR. This study presents a dataset (the AGR Dataset) for AGR scene recognition using image crawling, augmentation, and classification, which has been made available to the community. The AGR dataset crawled over 3k images from 13 databases (over 26k images after augmentation), and different aircraft, illumination, and environmental conditions were included. The ground-truth labeling is conducted manually using a proposed tree-formed decision workflow and six specific AGR tags. Various professionals have independently reviewed the AGR dataset to keep it no-bias. This study proposes the first aircraft refueling image dataset, and an image labeling software with a UI to automate the labeling workflow.
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    Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform
    (Elsevier Science B.V., Amsterdam, 2014-06-10) Czapinski, Michal; Barnes, Stuart
    The introduction of NVidia's powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As aresult, existing algorithms implemented on aGPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. AGPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.