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Title	Intelligent German Traffic Sign And Road Barrier Assist For Autonomous Driving In Smart Cities
ID_Doc	32396
Authors	Hegde S.K.; Dharmalingam R.; Kannan S.
Year	2024
Published	Multimedia Tools and Applications, 83, 22
DOI	http://dx.doi.org/10.1007/s11042-023-16435-1
Abstract	Smart cities are becoming an essential part in most of the countries. Safety also becomes important when such transformations occur. Safety can be achieved with the help of autonomous vehicles. Adoption of AI has provided practical solutions to most of the autonomous driving problems. Extended Traffic sign recognition is important for advanced driver assistance systems, autonomous vehicles, and mobile robots to inform the driver about the potential hazard ahead. Several research on traffic sign recognition has been carried out in the past, and most existing works were focused on traffic signs. Although research works have been done for detecting traffic signs, still there is a need for detection of small construction zone traffic signs and road barriers with better accuracy. This paper aims to showcase the results obtained using state of art deep networks, namely DSOD, a variant of SSD, YOLOV3, YOLOv4 for detection of construction zone traffic signs from the video sequences captured by a camera mounted on a car. Further, the classification of signs is carried out using CNN, which uses the detector feature as input. In addition to that, tracking is performed to include the missed detections and exclude the false positives arrived during detection. The proposed approach can achieve a maximum of 90% accuracy with 0.5 IOU for the detection of construction zone signs and road barriers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Author Keywords	Autonomous driving; Classification; Extended traffic sign recognition; Object recognition; Single shot multibox detector; Tracking; yolov4

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