| DOI | Resolve DOI: https://doi.org/10.1109/CCECE47787.2020.9255732 |
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| Author | Search for: Hofer, Elizabeth1; Search for: Rahman, Taufiq1; Search for: Myers, Ryan1; Search for: Hamieh, Ismail1 |
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| Affiliation | - National Research Council of Canada. Automotive and Surface Transportation
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| Format | Text, Article |
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| Conference | 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Aug. 30-Sept 1, 2020, London, Ontario |
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| Subject | infrared; neural network; lane marking; detection; perception; autonomous driving |
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| Abstract | The retro-reflective characteristics of lane demarcations on roadways can potentially provide robust detection in the infrared spectrum even in poor lighting and weather conditions. This paper explores this idea by training a convolutional neural network using Darknet with YOLO to detect 9 classes of road lines from the Berkeley Deep Drive Dataset (BDD). Although BDD is composed of conventional colour images, they were converted to greyscale prior to training as a solution to the scarcity of datasets in the infrared spectrum. The trained model was evaluated on road scenes acquired by the infrared sensor of an Intel-Realsense camera. From the experimental results, it is concluded that object detection techniques primarily developed for localization and classification of objects in the form of bounding boxes are inherently unsuitable for detecting line shaped objects such roadway lane demarcations. In addition, despite the sub-optimal training and detection approach, the performance showed potential for robust lane detection using infrared images. |
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| Publication date | 2020-11-19 |
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| Publisher | IEEE |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 43102a06-ed68-467c-9cb3-d9cde7585ae0 |
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| Record created | 2021-12-22 |
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| Record modified | 2021-12-22 |
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