DEVELOPMENT OF REAL-TIME TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK USING DEEP LEARNING TECHNIQUES

Özet

Traffic signs are a mandatory feature of road traffic regulations worldwide. They are responsible for slowing down the speed and carrying out many other valuable duties, notifying drivers about dangerous parts of the route, signaling traffic destination, prohibiting, or allowing passage. In this way, traffic is smoother, it becomes better regulated and drivers understand, mark, and interpret the rules well. For this purpose, Machine Learning (ML) study is carried out with the Deep Learning (DL) approach, the Real-Time (RT) Traffic Signs Recognition (TSR) is successfully developed, and a 99,68% test accuracy is obtained which has been gradually built on autonomous vehicles. It is developed to alert the driver for traffic signs appearing on the road and it is assisted the driver reach the speed limit set in the section of the lane, ride, overtaking, etc. The developed TSR helps to boost safety dramatically on the way to autonomous driving. The system is built on the DL, and it is trained with the Convolutional Neural Network (CNN) model to a classifier and predicts the status which is very effective for image classification purposes, and it is the most common and lovable algorithm for image data processing. It is also visualized how the accuracy and loss rates have changed over time. Later, it is implemented the graphical Unit Interface (GUI) were to show the results and draw the accuracy and the loss graphs. To realize classification as RT, Computer Vision (CV) approach is also included within the developed software to support camera viewing and understanding digital images.