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Title	Carriageway Edge Detection For Unmarked Urban Roads Using Deep Learning Techniques
ID_Doc	13429
Authors	Vasudha E.; Chilukuri B.R.
Year	2023
Published	2023 Smart Cities Symposium Prague, SCSP 2023
DOI	http://dx.doi.org/10.1109/SCSP58044.2023.10146209
Abstract	Traffic infrastructure segmentation is widely used in for digitization of urban areas for smart city applications. In this paper, we use transfer learning for carriageway edge detection for Indian urban roads where lane edge markings are often invisible. Also, the edge of the pavement often overlaps with unpaved shoulders posing challenges for image-based edge detection. The images obtained from various routes in Chennai, India are annotated and their masks were trained using DeepLabV3with ResNet101 and MobileNetV3 as backbones to predict the classes for each pixel. Two performance metrics, AUROC and F1 score, were used to evaluate the methods. Results show that DeepLabV3 with ResNet101 outperformed DeepLabV3 with MobileNetV3. © 2023 IEEE.
Author Keywords	Binary semantic segmentation; DeepLabV3; Pavement edge; Unmarked Pavements

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