REAL-TIME DETECTION OF TRAFFIC SIGNS WITH YOLO ALGORITHMS

Year 2025, Volume: 13 Issue: 1, 220 - 237, 01.03.2025

Abdil Karakan

https://doi.org/10.36306/konjes.1531208

Abstract

The number of electric vehicles is increasing day by day. The biggest reason for the increase in electric vehicles is their autonomous or semi-autonomous use feature. Autonomous or semi-autonomous driving; It is the movement of the vehicle with the data coming from the sensors, cameras, and sensors around the vehicle. The majority of traffic accidents are caused by driver errors. The most important of these mistakes is not obeying traffic rules. Autonomous or semi-autonomous driving largely prevents driver-related traffic accidents. The biggest problem of autonomous vehicles is the difficulties in detecting traffic signs in real-time. The locations, shapes, and scales of traffic signs are very different. Traffic signs are difficult to detect in real-world conditions due to their similarity to other objects. The study carried out real-time detection of traffic signs. For this purpose, images were taken from the camera placed inside the vehicle. A data set was created with these images. The more real environment images the data set consists of, the more accurate the real-time detection process increases. In this study, 8931 traffic sign images were taken from real environments. These images were taken from different locations, different lighting levels, and different distances. In addition, the number of data was increased to 78895 by adding grayscale, adding slope, blurring, adding variability, adding noise, changing image brightness, changing colour vividness, changing perspective, resizing, and positioning the images. With this study, the data set was adapted to the real environment. The created data set was used in 3 different versions of YOLOv5 architecture, YOLOv6, YOLOv7 and YOLOv8 architectures. As a result of the study, the highest accuracy was found to be 99.60%, F1-Score was 0.962 and mAP@.5 value was 0.993 in YOLOv8 architecture.

Keywords

Deep Learning, Real-Time Detection, Traffic Signs, YOLO

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