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YOLO, Xception, preprocessing, data augmentation, template matching.
Deep learning (DL) architectures are becoming increasingly popular in modern traffic systems and self-driven vehicles owing to their high efficiency and accuracy. Emerging technological advancements and the availability of large databases have made a favorable impact on such improvements. In this study, we present a traffic sign recognition system based on novel DL architectures, trained and tested on a locally collected traffic sign database. Our approach includes two stages; traffic sign identification from live video feed, and classification of each sign. The sign identification model was implemented with YOLO architecture and the classification model was implemented with Xception architecture. The input video feed for these models were collected using dashboard camera recordings. The classification model has been trained with the German Traffic Sign Recognition Benchmark dataset as well for comparison. Final accuracy of classification for the local dataset was 96.05% while the standard dataset has given an accuracy of 92.11%. The final model is a combination of the detection and classification algorithms and it is able to successfully detect and classify traffic signs from an input video feed within an average detection time of 4.5fps
Sithmini Gunasekara, Dilshan Gunarathna, Maheshi B. Dissanayake, Supavadee Aramith, Wazir Muhammad, "Deep Learning Based Autonomous Real-Time Traffic Sign Recognition System for Advanced Driver Assistance", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.6, pp. 70-83, 2022. DOI:10.5815/ijigsp.2022.06.06
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