Chen, Qianqian;
Xiao, Changshi;
Wen, Yuanqiao;
Yuan, Haiwen;
Huang, Yamin;
Liu, Yuanchang;
Zhang, Wenqiang;
(2023)
Knowledge-Driven Semantic Segmentation for Waterway Scene Perception.
IEEE Sensors Journal
10.1109/JSEN.2023.3304973.
(In press).
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Abstract
Semantic segmentation as one of the most popular scene perception techniques has been studied for autonomous vehicles. However, deep learning-based solutions rely on the volume and quality of data and knowledge from specific scene might not be incorporated. A novel knowledge-driven semantic segmentation method is proposed for waterway scene perception. Based on the knowledge that water is irregular and dynamically changing, a Life Time of Feature (LToF) detector is designed to distinguish water region from surrounding scene. Using a Bayesian framework, the detector as the likelihood function is combined with U-Net based semantic segmentation to achieve an optimized solution. Finally, two public datasets and typical semantic segmentation networks, FlowNet, DeepLab and DVSNet are selected to evaluate the proposed method. Also, the sensitivity of these methods and ours to dataset is discussed.
| Type: | Article |
|---|---|
| Title: | Knowledge-Driven Semantic Segmentation for Waterway Scene Perception |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JSEN.2023.3304973 |
| Publisher version: | https://doi.org/10.1109/JSEN.2023.3304973 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Semantic segmentation, Feature extraction, Sensors, Detectors, Semantics, Optical flow, Probabilistic logic |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10175536 |
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