Abstract
The system of detecting and recognizing the traffic signs is an important component of the system of smart transportation. Having the ability to detect the signs related to traffic precisely and correctly will improve safety of driving. This particular paper provides a deep learning-based detecting and recognizing of signs related to traffic methods with the primary purpose of recognizing and classifying circular signs. To begin, a picture is pre-processed to information which is crucial. Second, Hough rework is utilized in police investigations and area mapping. Finally, deep learning is employed to map the road traffic signs that have been detected. This book proposes an image-based traffic sign detection and identification technology that is subsequently merged with a convolutional neural network to type traffic signals. Because of its major identification rate, CNN can be utilized to recognize a wide range of computer vision tasks.
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Kumar, P.P., Kishen, R.C., Ravikumar, M. (2022). Traffic Sign Detection and Recognition Using Deep Learning. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2350-0_34
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DOI: https://doi.org/10.1007/978-981-19-2350-0_34
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