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Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models

Autor: Shah, Imad Ali, Li, Jiarong, Glavin, Martin, Jones, Edward, Ward, Enda, Deegan, Brian

Hyperspectral Imaging (HSI) is known for its advantages over traditional RGB imaging in remote sensing, agriculture, and medicine. Recently, it has gained attention for enhancing Advanced Driving Assistance Systems (ADAS) perception. Several HSI data

Externí odkaz: http://arxiv.org/abs/2410.22101

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection

Autor: Jakab, Daniel, Braun, Alexander, Agnew, Cathaoir, Mohandas, Reenu, Deegan, Brian Michael, Molloy, Dara, Ward, Enda, Scanlan, Tony, Eising, Ciarán

Publikováno v: Proceedings of the Irish Machine Vision and Image Processing Conference 2024

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation

Externí odkaz: http://arxiv.org/abs/2407.15646

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Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges

Autor: Jakab, Daniel, Deegan, Brian Michael, Sharma, Sushil, Grua, Eoin Martino, Horgan, Jonathan, Ward, Enda, Van De Ven, Pepijn, Scanlan, Anthony, Eising, Ciarán

Publikováno v: IEEE Transactions on Intelligent Transportation Systems, 2024

In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-

Externí odkaz: http://arxiv.org/abs/2402.12041

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