Examining the Performance of a Deep Learning Model Utilizing Yolov8 for Vehicle Make and Model Classification

Öz

Vehicles are important inventions that greatly improve various aspects of human life and find use in almost every field. Once tools are introduced to human existence, they enable time-saving and tasks that are complex or cannot be accomplished by human power. It can be used in situations such as classification of vehicles and tracking of escaped drivers. Tracking the vehicles with the help of brand and model will provide distinctive information to traffic officers. In addition, vehicles of different sizes and functions in traffic can be directed to different lanes. This study examines the use of a YOLOv8 (You Only Look Once version 8) based deep learning model and evaluates its performance for vehicle brand and model classification. YOLOv8 is known as an effective method in the field of object detection and is used in this study to classify the make and model of vehicles. In the classification, 94.3% classification accuracy was achieved.

Anahtar Kelimeler

• Vehicle make and model recognition
• deep learning
• YOLOv8
• classification

Kaynakça

1. [1] Lee, S. et al., “Intelligent traffic control for autonomous vehicle systems based on machine learning”, Expert Systems with Applications 144 (2020) : 113074.
2. [2] Wang, C., Cheng, J., Wang, Y., Qian, Y., “Hierarchical scheme for vehicle make and model recognition”, Transportation Research Record 2675(7) (2021) : 363–376.
3. [3] Tas, S., Sari, O., Dalveren, Y., Pazar, S., Kara, A., Derawi, M., “Deep learning-based vehicle classification for low quality images”, Sensors 22(13) (2022) : 4740.
4. [4] Ali, M., Tahir, M.A., Durrani, M.N., “Vehicle images dataset for make and model recognition”, Data in Brief 42 (2022) : 108107.
5. [5] Manzoor, M.A., Morgan, Y., Bais, A., “Real-time vehicle make and model recognition system”, Machine Learning and Knowledge Extraction 1(2) (2019) : 611–629.
6. [6] Hassan, A., Ali, M., Durrani, N.M., Tahir, M.A., “An empirical analysis of deep learning architectures for vehicle make and model recognition”, IEEE Access 9 (2021) : 91487–91499
7. [7] Bhujbal, A., Mane, D.T., “Vehicle type classification using deep learning”, in Soft Computing and Signal Processing: Proceedings of 2nd ICSCSP 2019 2. Springer Singapore (2020) : 279-290.
8. [8] Ren, Y., Lan, S., “Vehicle make and model recognition based on convolutional neural networks”, in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (2016) : 692–695.

9. [9] Luo, X., Shen, R., Hu, J., Deng, J., Hu, L., Guan, Q., “A deep convolution neural network model for vehicle recognition and face recognition”, Procedia Computer Science 107 (2017) : 715–720.
10. [10] Jamil, A.A., Hussain, F., Yousaf, M.H., Butt, A.M., Velastin, S.A., “Vehicle make and model recognition using bag of expressions”, Sensors 20(4) (2020) : 1033.
11. [11] Abbas, A.F., Sheikh, U.U., Mohd, M.N.H., “Recognition of vehicle make and model in low light conditions”, Bulletin of Electrical Engineering and Informatics 9(2) (2020) : 550-557.
12. [12] Fomin, I., Nenahov, I., Bakhshiev, A., “Hierarchical system for car make and model recognition on image using neural networks”, in 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) Sochi, Russia: IEEE (2020) : 1–6.
13. [13] Ni, X., Huttunen, H., “Vehicle attribute recognition by appearance: computer vision methods for vehicle type, make and model classification”, J Sign Process Syst 93(4) (2021) : 357–368.
14. [14] Boonsim, N., Prakoonwit, S., “Car make and model recognition under limited lighting conditions at night”, Pattern Anal Applic 20(4) (2017) : 1195–1207.
15. [15] Tavanaei, A., Ghodrati, M., Kheradpisheh, S.R., Masquelier, T., Maida, A., “Deep learning in spiking neural networks”, Neural Networks 111 (2019) : 47–63.
16. [16] Redmon, J., Divvala, S., Girshick, R., Farhadi, A., “You only look once: unified, real- time object detection”, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA: IEEE (2016) : 779–788.
17. [17] Karaci, A., “X-ışını görüntülerinden omuz implantlarının tespiti ve sınıflandırılması: YOLO ve önceden eğitilmiş evrişimsel sinir ağı tabanlı bir yaklaşım”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37(1) (2021) : 283–294.
18. [18] Öztürkoğlu, M., “Predicting various architectural styles using computer vision methods”, MBUD (2023) : 811–828.
19. [19] Gao, X., Zhang, Y., “Detection of fruit using YOLOv8-based single stage detectors”, IJACSA 14 (2023) : 83-91.
20. [20] Göde, A., Kalkan, A., “Performance comparison machine learning algorithms in diabetes disease prediction”, European Mechanical Science 7(3) (2023) : 178-183.
21. [21] Aksoy, S., Özavsar, M., Altındal, A., “Classification of VOC vapors using machine learning algorithms”, Journal of Engineering Technology and Applied Sciences 7(2) (2022) : 97-107.