The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Spatial as Deep: Spatial CNN for Traffic Scene Understanding
Open accessAbstract
Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-by-slice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.
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Cites
Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
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“works (Zheng et al. 2015; Liu et al. 2015; Chen et al. 2017) to combine them with CNN all follow the pipeline of Fig.”
MethodThe Cityscapes Dataset for Semantic Urban Scene Understanding
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Cited by
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & de Albuquerque, V. H. C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032227 · Cited by 659
Learning From Synthetic Data for Crowd Counting in the Wild
Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00839 · Cited by 654
Ultra Fast Structure-Aware Deep Lane Detection
Qin, Z., Wang, H., & Li, X. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_17 · Cited by 554
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
Tabelini, L., Berriel, R., Paixao, T. M., Badue, C., De Souza, A. F., & Oliveira-Santos, T. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00036 · Cited by 478
YOLOP: You Only Look Once for Panoptic Driving Perception
Wu, D., Liao, M.-W., Zhang, W.-T., Wang, X.-G., Bai, X., Cheng, W.-Q., & Liu, W.-Y. (2022) · Machine Intelligence Research · doi:10.1007/s11633-022-1339-y · Cited by 466
Self-citation
Metadata
- Authors
- Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-27
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2780740184
- Cited by
- 1,177
- References
- 12
- FWCI
- 24.15
- Type
- conference paper
- Language
- en
- Open access
- diamond
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