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How to deal with glare for improved perception of Autonomous Vehicles (2404.10992v1)

Published 17 Apr 2024 in cs.CV

Abstract: Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played an important role in adopting vision-based environment perception systems in autonomous vehicles (AVs). However, vision-based perception systems can be easily affected by glare in the presence of a bright source of light, such as the sun or the headlights of the oncoming vehicle at night or simply by light reflecting off snow or ice-covered surfaces; scenarios encountered frequently during driving. In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs. We evaluate these glare reduction methods based on various performance metrics of the CV algorithms used by the perception layer. Specifically, we considered object detection, object recognition, object tracking, depth estimation, and lane detection which are crucial for autonomous driving. The experimental findings validate the efficacy of the proposed glare reduction approach, showcasing enhanced performance across diverse perception tasks and remarkable resilience against varying levels of glare.

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References (36)
  1. “Object detection for connected and autonomous vehicles using cnn with attention mechanism,” in 2022 IEEE Vehicular Technology Conference, 2022, pp. 1–6.
  2. “Talking about automated vehicles: What do levels of automation do?,” Technology in Society, vol. 64, pp. 101488, 2021.
  3. “Vision-based autonomous driving: A hierarchical reinforcement learning approach,” IEEE Transactions on Vehicular Technology, vol. 72, pp. 11213–11226, 2023.
  4. “Drivable region estimation for self-driving vehicles using radar,” IEEE Transactions on Vehicular Technology, vol. 71, pp. 5971–5982, 2022.
  5. “Reduction of glare in images with saturated pixels,” in 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), 2021, pp. 498–502.
  6. “Single image haze removal using dark channel prior,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 12, pp. 2341–2353, 2010.
  7. “Liquid-filled camera for the measurement of high-contrast images,” in Cockpit Displays X, Darrel G. Hopper, Ed. International Society for Optics and Photonics, 2003, vol. 5080, pp. 370 – 378.
  8. “Removal of glare caused by water droplets,” in 2009 Conference for Visual Media Production, Nov 2009, pp. 144–151.
  9. High Dynamic Range Imaging - Acquisition, Display and Image-based Lighting, Morgan Kaufman Publishers, 2006.
  10. “Veiling glare in high dynamic range imaging,” in ACM SIGGRAPH 2007 Papers. 2007, p. 37–es, Association for Computing Machinery.
  11. “Fast separation of direct and global components of a scene using high frequency illumination,” in ACM SIGGRAPH 2006, 2006, p. 935–944.
  12. “Glare aware photography: 4d ray sampling for reducing glare effects of camera lenses,” ACM Trans. Graph., vol. 27, pp. 1–10, 2008.
  13. “Dehazenet: An end-to-end system for single image haze removal,” IEEE Transactions on Image Processing, vol. 25, pp. 5187–5198, 2016.
  14. “Proximal dehaze-net: A prior learning-based deep network for single image dehazing,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
  15. H. Zhang and V. M. Patel, “Densely connected pyramid dehazing network,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 3194–3203.
  16. “Pms-net: Robust haze removal based on patch map for single images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
  17. “Infwide: Image and feature space wiener deconvolution network for non-blind image deblurring in low-light conditions,” IEEE Transactions on Image Processing, vol. 32, pp. 1390–1402, 2023.
  18. “Sharpformer: Learning local feature preserving global representations for image deblurring,” IEEE Transactions on Image Processing, vol. 32, pp. 2857–2866, 2023.
  19. “Space-variant blur kernel estimation and image deblurring through kernel clustering,” Signal Processing: Image Communication, vol. 76, pp. 41 – 55, 2019.
  20. “Learning a convolutional neural network for non-uniform motion blur removal,” in IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2015, pp. 769–777.
  21. “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2017, pp. 3883–3891.
  22. “Light field extraction from a conventional camera,” Signal Processing: Image Communication, vol. 109, pp. 116845, 2022.
  23. “Hdr4cv: High dynamic range dataset with adversarial illumination for testing computer vision methods,” in London Imaging Meeting. Society for Imaging Science and Technology, 2021, vol. 2021, pp. 40404–1.
  24. “Demosaicing with directional filtering and a posteriori decision,” IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 132–141, 2006.
  25. “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271.
  26. “Detecting twenty-thousand classes using image-level supervision,” ECCV, 2022.
  27. “Simple online and realtime tracking with a deep association metric,” in 2017 IEEE international conference on image processing (ICIP). IEEE, 2017, pp. 3645–3649.
  28. “Spatial as deep: Spatial cnn for traffic scene understanding,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2018, vol. 32.
  29. “Learning light fields for improved lane detection,” IEEE Access, vol. 11, pp. 271–283, 2022.
  30. “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” CoRR, vol. abs/1907.01341, 2019.
  31. “Attention transfer from human to neural networks for road object detection in winter,” IET Image Processing, vol. 16, no. 13, pp. 3544–3556, 2022.
  32. “Curricular contrastive regularization for physics-aware single image dehazing,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
  33. “Progressive feature fusion network for realistic image dehazing,” in Asian Conference on Computer Vision (ACCV), 2018.
  34. “Fast single image reflection suppression via convex optimization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8141–8149.
  35. “Blind deblurring for saturated images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 6308–6316.
  36. “Reconfiguring the imaging pipeline for computer vision,” in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 975–984.
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Authors (3)
  1. Muhammad Z. Alam (1 paper)
  2. Zeeshan Kaleem (13 papers)
  3. Sousso Kelouwani (1 paper)

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