Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment

Afdhal Afdhal; Khairun Saddami; Sugiarto Sugiarto; Zahrul Fuadi; Nasaruddin Nasaruddin

doi:10.1109/COSITE60233.2023.10249521

Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment

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Afdhal Afdhal, Khairun Saddami, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

2023 Proceeding - 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering: Sustainable Development for Smart Innovation System, COSITE 2023 Conference paper Cited by 43

Abstract

Self-driving cars have gained significant attention in recent years. Real-time object detection is a critical component of their perception system. One of the main challenges in developing safe and efficient self-driving cars lies in accurately and in real-time detecting objects in diverse and complex traffic environments. This paper presents the performance evaluation of real-time object detection of YOLOv8 models, a state-of-the-art deep learning framework for self-driving cars in mixed traffic environments. The objective is to assess the extent of YOLOv8's object detection capabilities can be used for self-driving car within complex realworld traffic scenarios. The experimental results show that the accuracy of YOLOv8 during normal daylight scenarios ranges from 0.60 ~ 0.80. In contrast, the accuracy values obtained in night scenarios fall between 0.15 ~ 0.25. Similarly, the FMeasure of YOLOv8 models under daylight conditions ranges from 0.75 ~ 0.87. Conversely, the F-Measure in night conditions falls between 0.27 ~ 0.46. Based on our thorough evaluation results, YOLOv8 has demonstrated its robustness in detecting objects. However, the algorithm requires improvement to effectively handle the challenges of self-driving cars' object detection systems in mixed traffic, such as diverse object classifications, small-scale objects, fast-moving objects, blur, glare, and low-light illumination, particularly in nighttime scenarios. © 2023 IEEE.

Affiliations

Doctoral Program, School of Engineering, Universitas Syiah Kuala, Indonesia; Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Indonesia; Department of Civil Engineering, Universitas Syiah Kuala, Indonesia; Department of Mechanical dan Industrial Engineering, Universitas Syiah Kuala, Indonesia; Telematics Research Center (TRC), Indonesia

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