Occlusion-free road segmentation leveraging semantics for autonomous vehicles
The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive under...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142140 |
| _version_ | 1826128990540136448 |
|---|---|
| author | Wang, Kewei Yan, Fuwu Zou, Bin Tang, Luqi Yuan, Quan Lv, Chen |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Wang, Kewei Yan, Fuwu Zou, Bin Tang, Luqi Yuan, Quan Lv, Chen |
| author_sort | Wang, Kewei |
| collection | NTU |
| description | The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time. |
| first_indexed | 2024-10-01T07:33:33Z |
| format | Journal Article |
| id | ntu-10356/142140 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:33:33Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1421402023-03-04T17:23:05Z Occlusion-free road segmentation leveraging semantics for autonomous vehicles Wang, Kewei Yan, Fuwu Zou, Bin Tang, Luqi Yuan, Quan Lv, Chen School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Autonomous Vehicles Scene Understanding The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time. Published version 2020-06-16T06:09:56Z 2020-06-16T06:09:56Z 2019 Journal Article Wang, K., Yan, F., Zou, B., Tang, L., Yuan, Q., & Lv, C. (2019). Occlusion-free road segmentation leveraging semantics for autonomous vehicles. Sensors, 19(21), 4711-. doi:10.3390/s19214711 1424-8220 https://hdl.handle.net/10356/142140 10.3390/s19214711 31671547 2-s2.0-85074324216 21 19 en Sensors © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Mechanical engineering Autonomous Vehicles Scene Understanding Wang, Kewei Yan, Fuwu Zou, Bin Tang, Luqi Yuan, Quan Lv, Chen Occlusion-free road segmentation leveraging semantics for autonomous vehicles |
| title | Occlusion-free road segmentation leveraging semantics for autonomous vehicles |
| title_full | Occlusion-free road segmentation leveraging semantics for autonomous vehicles |
| title_fullStr | Occlusion-free road segmentation leveraging semantics for autonomous vehicles |
| title_full_unstemmed | Occlusion-free road segmentation leveraging semantics for autonomous vehicles |
| title_short | Occlusion-free road segmentation leveraging semantics for autonomous vehicles |
| title_sort | occlusion free road segmentation leveraging semantics for autonomous vehicles |
| topic | Engineering::Mechanical engineering Autonomous Vehicles Scene Understanding |
| url | https://hdl.handle.net/10356/142140 |
| work_keys_str_mv | AT wangkewei occlusionfreeroadsegmentationleveragingsemanticsforautonomousvehicles AT yanfuwu occlusionfreeroadsegmentationleveragingsemanticsforautonomousvehicles AT zoubin occlusionfreeroadsegmentationleveragingsemanticsforautonomousvehicles AT tangluqi occlusionfreeroadsegmentationleveragingsemanticsforautonomousvehicles AT yuanquan occlusionfreeroadsegmentationleveragingsemanticsforautonomousvehicles AT lvchen occlusionfreeroadsegmentationleveragingsemanticsforautonomousvehicles |