Traffic Sign Detection and Recognition Using Deep Learning (Yolov5) |
Author(s): |
| Rahul Bhakad , Sinhgad Academy of Engineering; Shantanu Bawankule, Sinhgad Academy of Engineering; Kunal Gaikwad, Sinhgad Academy of Engineering; P.R.Dongare, Sinhgad Academy of Engineering |
Keywords: |
| YOLO, Convolutional Neural Network, CNN, Google Colab, Roboflow, Deep Learning, Machine Learning, Object Detection, Text-To-Speech Conversion, Pyttsx3 |
Abstract |
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Traffic signs are important to ensure smooth traffic flow without mishaps. Traffic symbols are the pictorial representations having different necessary information required to be understood by the driver. Traffic signs in front of the vehicle are ignored by the drivers and this can lead to catastrophic accidents. To tackle this problem, we implemented a deep learning model based on YOLO’s (You Only Look Once) latest version YOLOv5 in this project. YOLO is one of the fastest object detection algorithms for real-time detection. The goal is to find and classify traffic signs in real-world street settings. This paper explains the various steps to implement the YOLOv5 for detecting and recognizing different types of traffic signs and help the driver for safe navigation. |
Other Details |
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Paper ID: IJSRDV10I30236 Published in: Volume : 10, Issue : 3 Publication Date: 01/06/2022 Page(s): 388-391 |
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