An Implementation of Traffic Signs and Road Objects Detection Using Faster R-CNN

Emin Güney; Cüneyt Bayılmış

doi:10.35377/saucis...1073355

Research Article

An Implementation of Traffic Signs and Road Objects Detection Using Faster R-CNN

Year 2022, Volume: 5 Issue: 2, 216 - 224, 31.08.2022

Emin Güney Cüneyt Bayılmış

https://doi.org/10.35377/saucis...1073355

Cited By: 5

Abstract

Traffic signs and road objects detection is significant issue for driver safety. It has become popular with the development of autonomous vehicles and driver-assistant systems. This study presents a real-time system that detects traffic signs and various objects in the driving environment with a camera. Faster R-CNN architecture was used as a detection method in this study. This architecture is a well-known two-stage approach for object detection. Dataset was created by collecting various images for training and testing of the model. The dataset consists of 1880 images containing traffic signs and objects collected from Turkey with the GTSRB dataset. These images were combined and divided into the training set and testing set with the ratio of 80/20. The model's training was carried out in the computer environment for 8.5 hours and approximately 10000 iterations. Experimental results show the real-time performance of Faster R-CNN for robustly traffic signs and objects detection.

Keywords

deep learning, traffic sign detection and recognition (TSDR), object detection, faster r-cnn

Supporting Institution

Sakarya University Scientific Research Projects Coordination Unit

Project Number

2021-7-24-20

Thanks

We want to thank our hardworking teammates Neslihan ÇAKIRBAŞ, Havva Selin ÇAKMAK, Ali Göktuğ YALÇIN and Dilara KOCA. They helped us prepare the datasets and the development of the system.

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