Wang, H.; Li, Z.; Li, J. Road Car Image Target Detection and Recognition Based on YOLOv8 Deep Learning Algorithm. Applied and Computational Engineering 2024, 69, 103–108, doi:10.54254/2755-2721/69/20241489.
Wang, H.; Li, Z.; Li, J. Road Car Image Target Detection and Recognition Based on YOLOv8 Deep Learning Algorithm. Applied and Computational Engineering 2024, 69, 103–108, doi:10.54254/2755-2721/69/20241489.
Wang, H.; Li, Z.; Li, J. Road Car Image Target Detection and Recognition Based on YOLOv8 Deep Learning Algorithm. Applied and Computational Engineering 2024, 69, 103–108, doi:10.54254/2755-2721/69/20241489.
Wang, H.; Li, Z.; Li, J. Road Car Image Target Detection and Recognition Based on YOLOv8 Deep Learning Algorithm. Applied and Computational Engineering 2024, 69, 103–108, doi:10.54254/2755-2721/69/20241489.
Abstract
In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.
Keywords
YOLOv8; Deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
