Abstract
Lane detection represents a fundamental task within autonomous driving. While deep learning has made remarkable advancements in the source domain, its ability to generalize to unseen target domains still poses a challenge. To address this issue, we present a Fourier-based instance selective whitening framework. This framework utilizes the distinct frequencies within the Fourier spectrum to decompose data style into environment and texture styles. Our method preserves semantic features by stabilizing the phase component, while also extending the style through perturbing and amalgamating the amplitude component. Further, we propose a standardized instance selective whitening strategy to analyze overall distributional changes, emphasizing general features and reducing domain-specific information. Our approach is validated through extensive experiments across multiple challenging datasets, such as Tusimple, CULane, and LLAMAS, which demonstrates significant effectiveness when compared to existing methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tusimple. Tusimple benchmark (2017)
Araslanov, N., Roth, S.: Self-supervised augmentation consistency for adapting semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15384–15394 (2021)
Balaji, Y., Sankaranarayanan, S., Chellappa, R.: Metareg: towards domain generalization using meta-regularization. Advances in neural information processing systems 31 (2018)
Behrendt, K., Soussan, R.: Unsupervised labeled lane markers using maps. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)
Cho, W., Choi, S., Park, D.K., Shin, I., Choo, J.: Image-to-image translation via group-wise deep whitening-and-coloring transformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10639–10647 (2019)
Choi, S., Jung, S., Yun, H., Kim, J.T., Kim, S., Choo, J.: Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11580–11590 (2021)
Dou, Q., Coelho de Castro, D., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. Advances in neural information processing systems 32 (2019)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2030 (2016)
Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. Advances in neural information processing systems 28 (2015)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D.: Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2551–2559 (2015)
Gong, R., Li, W., Chen, Y., Gool, L.V.: Dlow: domain flow for adaptation and generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2477–2486 (2019)
Hansen, B.C., Hess, R.F.: Structural sparseness and spatial phase alignment in natural scenes. JOSA A 24(7), 1873–1885 (2007)
Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNs by self attention distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1013–1021 (2019)
Hoyer, L., Dai, D., Van Gool, L.: Daformer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9924–9935 (2022)
Lee, S., Seong, H., Lee, S., Kim, E.: Wildnet: Learning domain generalized semantic segmentation from the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9936–9946 (2022)
Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: Meta-learning for domain generalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Li, H., Pan, S.J., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5400–5409 (2018)
Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Universal style transfer via feature transforms. Advances in neural information processing systems 30 (2017)
Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.: Unified deep supervised domain adaptation and generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5715–5725 (2017)
Nussbaumer, H.J., Nussbaumer, H.J.: The fast Fourier transform. Springer (1982)
Oppenheim, A., Lim, J., Kopec, G., Pohlig, S.: Phase in speech and pictures. In: ICASSP’79. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 632–637. IEEE (1979)
Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69(5), 529–541 (1981)
Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via ibn-net. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 464–479 (2018)
Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: spatial CNN for traffic scene understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Pan, X., Zhan, X., Shi, J., Tang, X., Luo, P.: Switchable whitening for deep representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1863–1871 (2019)
Piotrowski, L.N., Campbell, F.W.: A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11(3), 337–346 (1982)
Rahman, M.M., Fookes, C., Baktashmotlagh, M., Sridharan, S.: Correlation-aware adversarial domain adaptation and generalization. Pattern Recogn. 100, 107124 (2020)
Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: Erfnet: efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 19(1), 263–272 (2017)
Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9471–9480 (2019)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211–252 (2015)
Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for gans. arXiv preprint arXiv:1806.00420 (2018)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517–2526 (2019)
Wang, Z., Ren, W., Qiu, Q.: Lanenet: real-time lane detection networks for autonomous driving. arXiv preprint arXiv:1807.01726 (2018)
Wu, D., et al.: Yolop: you only look once for panoptic driving perception. Mach. Intell. Res. 19(6), 550–562 (2022)
Xu, Q., Zhang, R., Zhang, Y., Wang, Y., Tian, Q.: A fourier-based framework for domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14383–14392 (2021)
Yang, Y., Soatto, S.: Fda: fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085–4095 (2020)
Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., Gong, B.: Domain randomization and pyramid consistency: simulation-to-real generalization without accessing target domain data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2100–2110 (2019)
Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., Wen, F.: Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12414–12424 (2021)
Zheng, T., et al.: Clrnet: cross layer refinement network for lane detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 898–907 (2022)
Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Learning to generate novel domains for domain generalization. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI 16, pp. 561–578. Springer (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, W., Wei, S., Xu, S., Tan, C., Zhang, S., Zhao, Y. (2024). Fourier-Based Instance Selective Whitening for Domain Generalized Lane Detection. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Computer Science and Technology. ICCSE 2023. Communications in Computer and Information Science, vol 2023. Springer, Singapore. https://doi.org/10.1007/978-981-97-0730-0_37
Download citation
DOI: https://doi.org/10.1007/978-981-97-0730-0_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0729-4
Online ISBN: 978-981-97-0730-0
eBook Packages: Computer ScienceComputer Science (R0)