A Fusion Model for Road Detection based on Deep Learning and Fully Connected CRF
Abstract
This paper presents a road detection model based on deep learning and fully connected condition random field to fuse image and point cloud data. Firstly, a convolutional neural network is trained to extract multi-scale features of the image. And a point-based deep neural network is trained to extract the multi-scale features of the point cloud. Secondly, the point cloud data is projected to the image plane. The probability maps of image and point cloud in the image plane are obtained by their corresponding multi-scale features, respectively. Thirdly, a Markov-based up-sampling method is used to get a dense height image from a sparse one which is from the point cloud data. A fully connected condition random field model based on the outputs of the two networks and the height image is constructed on the image plane. Finally, the fusion model is effectively solved by the mean-field approximate algorithm. Experiments on KITTI Road dataset show that the proposed model can effectively fuse the image and the point cloud data. Furthermore, the fusion model can also exclude the shadows, road curbs and other interferences in complex scenes.
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From the tables, most works propose to fuse LiDAR and camera features extracted from 2D convolution neural networks; several works cluster and segment 3D LiDAR points to generate 3D region proposals but still use a LiDAR 2D representation to extract features for fusion; several works project LiDAR points on the camera-plane or RGB camera images on the LiDAR BEV plane to align features, whereas许多许多
“From the tables we have three observations: (1). Most works propose to fuse LiDAR and camera features extracted from 2D convolution neural networks. To do this, they project LiDAR points on the 2D plane and process the feature maps through 2D convolutions. Only a few works extract LiDAR features by PointNet (e.g. [104], [105], [128]) or 3D convolutions (e.g. [123]); (2). Several works on multi”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [128]
Yang et al. (2018) [128] achieve road segmentation performance on KITTI test set of MaxF 91.40%, AP 84.22%, PRE 89.09%, REC 93.84%, FPR 6.33%, FNR 6.16%.
“Yang et al. , 2018 [128] | 91.40 % | 84.22 % | 89.09 % | 93.84 % | 6.33 % | 6.16 % | - | GPU |”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [128]
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Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
- Authors
- Yang, F., Yang, J., Jin, Z., & Wang, H.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Venue
- 2018 13th Annual Conference on System of Systems Engineering (SoSE)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 29-36
- OpenAlex
- W2885813678
- Cited by
- 13
- References
- 0
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 1 · 2021: 6 · 2020: 3
- FWCI
- 2.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on System of Systems Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Keywords
- road detection, image and point cloud fusion, convolutional neural networks, point cloud projection, multi-scale feature extraction, mean-field approximation
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