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RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal (2306.07244v1)

Published 12 Jun 2023 in cs.CV

Abstract: Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture.

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References (47)
  1. D-hazy: A dataset to evaluate quantitatively dehazing algorithms. In 2016 IEEE international conference on image processing (ICIP), pages 2226–2230. IEEE, 2016.
  2. I-haze: a dehazing benchmark with real hazy and haze-free indoor images. In International Conference on Advanced Concepts for Intelligent Vision Systems, pages 620–631. Springer, 2018.
  3. Single image dehazing by multi-scale fusion. IEEE Transactions on Image Processing, 22(8):3271–3282, 2013.
  4. A fast semi-inverse approach to detect and remove the haze from a single image. In Asian Conference on Computer Vision, pages 501–514. Springer, 2010.
  5. Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images. In 2019 IEEE international conference on image processing (ICIP), pages 1014–1018. IEEE, 2019.
  6. Nh-haze: An image dehazing benchmark with non-homogeneous hazy and haze-free images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 444–445, 2020.
  7. O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 754–762, 2018.
  8. Particulate matter exposure in agriculture. Air Pollution. A Comprehensive Perspective, 3, 2012.
  9. Non-local image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1674–1682, 2016.
  10. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11):5187–5198, 2016.
  11. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Transactions on Image Processing, 24(11):3888–3901, 2015.
  12. Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 10729–10736, 2020.
  13. Raanan Fattal. Single image dehazing. ACM transactions on graphics (TOG), 27(3):1–9, 2008.
  14. Raanan Fattal. Dehazing using color-lines. ACM transactions on graphics (TOG), 34(1):1–14, 2014.
  15. A fast image dehazing algorithm based on negative correction. Signal Processing, 103:380–398, 2014.
  16. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12):2341–2353, 2010.
  17. Single image dehazing of road scenes using spatially adaptive atmospheric point spread function. IEEE Access, 9:76135–76152, 2021.
  18. Harald Koschmieder. Theorie der horizontalen sichtweite. Beitrage zur Physik der freien Atmosphare, pages 33–53, 1924.
  19. Aod-net: All-in-one dehazing network. In Proceedings of the IEEE international conference on computer vision, pages 4770–4778, 2017.
  20. End-to-end united video dehazing and detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
  21. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1):492–505, 2018.
  22. Benchmarking detection transfer learning with vision transformers. arXiv preprint arXiv:2111.11429, 2021.
  23. Single image dehazing via model-based deep-learning. In 2022 IEEE International Conference on Image Processing (ICIP), pages 141–145. IEEE, 2022.
  24. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer, 2014.
  25. Towards multi-domain single image dehazing via test-time training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5831–5840, 2022.
  26. Mathworks. Peak signal-to-noise ratio (psnr) - matlab psnr - mathworks deutschland [online]. https://de.mathworks.com/help/images/ref/psnr.html. Accessed: 2022-11-03.
  27. Making a “completely blind” image quality analyzer. IEEE Signal processing letters, 20(3):209–212, 2012.
  28. Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012.
  29. Consistency guided network for degraded image classification. IEEE Transactions on Circuits and Systems for Video Technology, 31(6):2231–2246, 2020.
  30. Effects of image degradation and degradation removal to cnn-based image classification. IEEE transactions on pattern analysis and machine intelligence, 43(4):1239–1253, 2019.
  31. PyTorch. Faster r-cnn - torchvision main documentation [online]. https://pytorch.org/vision/main/models/faster_rcnn.html. Accessed: 2022-10-31.
  32. Enhanced pix2pix dehazing network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8160–8168, 2019.
  33. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
  34. Single image dehazing via multi-scale convolutional neural networks. In European conference on computer vision, pages 154–169. Springer, 2016.
  35. High-accuracy stereo depth maps using structured light. In 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., volume 1, pages I–I. IEEE, 2003.
  36. J Alex Stark. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on image processing, 9(5):889–896, 2000.
  37. Robby T Tan. Visibility in bad weather from a single image. In 2008 IEEE conference on computer vision and pattern recognition, pages 1–8. IEEE, 2008.
  38. Vision enhancement in homogeneous and heterogeneous fog. IEEE Intelligent Transportation Systems Magazine, 4(2):6–20, 2012.
  39. Improved visibility of road scene images under heterogeneous fog. In 2010 IEEE intelligent vehicles symposium, pages 478–485. IEEE, 2010.
  40. Cycle-snspgan: Towards real-world image dehazing via cycle spectral normalized soft likelihood estimation patch gan. IEEE Transactions on Intelligent Transportation Systems, 2022.
  41. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004.
  42. Self-augmented unpaired image dehazing via density and depth decomposition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2037–2046, 2022.
  43. Characteristics of agricultural dust emissions from harvesting operations: case study of a whole-feed peanut combine. Agriculture, 11(11):1068, 2021.
  44. Hazerd: an outdoor scene dataset and benchmark for single image dehazing. In 2017 IEEE international conference on image processing (ICIP), pages 3205–3209. IEEE, 2017.
  45. Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines. IEEE Transactions on Image Processing, 29:6947–6962, 2020.
  46. A fast single image haze removal algorithm using color attenuation prior. IEEE transactions on image processing, 24(11):3522–3533, 2015.
  47. Karel Zuiderveld. Contrast limited adaptive histogram equalization. Graphics gems, pages 474–485, 1994.
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