Vehicle Color-Type Detection Based on YOLOv8 and K-Means

İrem Çiçek; Nafiye Nur Apaydın; İrfan Kılıç; Orhan Yaman; Mehmet Karaköse

doi:10.54565/jphcfum.1951468

Vehicle Color-Type Detection Based on YOLOv8 and K-Means

Abstract

There is provided a computing device for managing honeybee colonies for pollination of crop(s), comprises: a processor(s) configured for: for each one of a plurality of honeybee colonies positioned for pollination of crop(s) in a geographical area: obtaining in-colony feature(s), computed based on output of internal sensor(s) monitoring the honeybee colony, wherein the in-colony feature(s) is indicative of an internal state of the honeybee colony positioned for pollination of crop(s) in the geographical area, obtaining out-colony feature(s), computed based on output of external sensor(s) monitoring the environment of the honeybee colony, wherein the out-colony feature is indicative of an external environment of the honeybee colony, feeding a combination of the in-colony feature(s) and the out-colony feature(s) into a machine learning model, and obtaining an outcome indicating pollination effectiveness of the honeybee colony.

Keywords

Supporting Institution

TUBITAK (The Scientific and Technological Research Council of Turkey) under

Project Number

Grant No: 5220154

Ethical Statement

The study is complied with research and publication ethics.

Thanks

This work was supported by the TUBITAK (The Scientific and Technological Research Council of Turkey) under Grant No: 5220154.

References

[1] C. Dalaff, “A Trafﬁc Object Detection System for Road Trafﬁc Measurement and Management”.
[2] A. Prahara ve Murinto, “Car detection based on road direction on traffic surveillance image”, içinde 2016 2nd International Conference on Science in Information Technology (ICSITech), Balikpapan, Indonesia: IEEE, Eki. 2016, ss. 344-349. doi: 10.1109/ICSITech.2016.7852660.
[3] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016.
[4] J. Redmon ve A. Farhadi, “YOLO9000: Better, Faster, Stronger”, 25 Dec. 2016, arXiv: arXiv:1612.08242. Accessed October 12, 2025.
[5] M. V Peppa, D. Bell, T. Komar, and W. Xiao, “URBAN TRAFFIC FLOW ANALYSIS BASED ON DEEP LEARNING CAR DETECTION FROM CCTV IMAGE SERIES,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII–4, pp. 499–506, 2018, doi: 10.5194/isprs-archives-XLII-4-499-2018.
[6] S. Sushmitha, N. Satheesh, and V. Kanchana, “Multiple Car Detection, Recognition and Tracking in Traffic,” in 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1–5. doi: 10.1109/INCET49848.2020.9154107.
[7] Q.-C. Mao, H.-M. Sun, L.-Q. Zuo, ve R.-S. Jia, “Finding every car: a traffic surveillance multi-scale vehicle object detection method”, Appl. Intell., c. 50, sy 10, ss. 3125-3136, Oct. 2020, doi: 10.1007/s10489-020-01704-5.
[8] B. Y. Suprapto, A. Ghaida, H. Hikmarika, ve S. Dwijayanti, “Road and Vehicles Detection System Using HSV Color Space for Autonomous Vehicle”, J. Ilm. Tek. Elektro Komput. Dan Inform., c. 6, sy 1, s. 42, Jul. 2020, doi: 10.26555/jiteki.v16i1.16949.

[9] V. Paidi, H. Fleyeh, J. Håkansson, ve R. G. Nyberg, “Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter”, J. Adv. Transp., c. 2021, ss. 1-12, Aug. 2021, doi: 10.1155/2021/1812647.
[10] C. Lv, U. Mittal, V. Madaan, ve P. Agrawal, “Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8”, PeerJ Comput. Sci., c. 10, s. e2233, Agu. 2024, doi: 10.7717/peerj-cs.2233.
[11] L. You, Y. Chen, C. Xiao, C. Sun, ve R. Li, “Multi-Object Vehicle Detection and Tracking Algorithm Based on Improved YOLOv8 and ByteTrack”, Electronics, c. 13, sy 15, s. 3033, Aug. 2024, doi: 10.3390/electronics13153033.
[12] G. E. Lima, R. Laroca, E. Santos, E. Nascimento, and D. Menotti, “Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark,” in 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2024, pp. 1–6. doi: 10.1109/SIBGRAPI62404.2024.10716307.
[13] Y.-L. Khor, Y. J. Wong, M.-L. Tham, Y. C. Chang, B.-H. Kwan, and K.-C. Khor, “Multi-Task YOLO for Vehicle Colour Recognition and Automatic License Plate Recognition,” in 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2024, pp. 1–7. doi: 10.1109/EAIS58494.2024.10570013.
[14] S. Ghanem and J. H. Holliman, “Impact of Color Space and Color Resolution on Vehicle Recognition Models,” J. Imaging, vol. 10, no. 7, 2024, doi: 10.3390/jimaging10070155.
[15] S. Sadiq, K. Sultan, M. Sheraz, C. C. Teong, and M. U. Hashmi, “Towards Efficient Vehicle Recognition: A Unified System for VMMR, ANPR, and Color Classification,” Computers, Materials, & Continua, vol. 85, no. 2, pp. 3945–3963, 2025, [Online]. Available: https://www.proquest.com/scholarly-journals/towards-efficient-vehicle-recognition-unified/docview/3259840849/se-2 [16] J. Redmon ve A. Farhadi, “YOLOv3: An Incremental Improvement”, 08 Apr. 2018, arXiv: arXiv:1804.02767. Accessed October 12, 2025.
[16] M. Swathi Y. and Challa, “YOLOv8: Advancements and Innovations in Object Detection,” in Smart Trends in Computing and Communications, C. and J. A. Senjyu Tomonobu and So–In, Ed., Singapore: Springer Nature Singapore, 2024, pp. 1–13.
[17] P. Shan, “Image segmentation method based on K-mean algorithm”, EURASIP J. Image Video Process., c. 2018, sy 1, s. 81, Dec. 2018, doi: 10.1186/s13640-018-0322-6.
[18] I. Boukraa, “Traffic Road Object Detection Dataset using YOLO,” 2023, Kaggle. Accessed: Mar. 28, 2026. [Online]. Available: https://www.kaggle.com/datasets/boukraailyesali/traffic-road-object-detection-dataset-using-yolo
[19] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” ArXiv, vol. abs/1804.02767, 2018, [Online]. Available: https://api.semanticscholar.org/CorpusID:4714433
[20] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” ArXiv, vol. abs/2004.10934, 2020, [Online]. Available: https://api.semanticscholar.org/CorpusID:216080778
[21] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Scaled-YOLOv4: Scaling Cross Stage Partial Network,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13024–13033, 2020, [Online]. Available: https://api.semanticscholar.org/CorpusID:226964445
[22] Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO Series in 2021,” ArXiv, vol. abs/2107.08430, 2021, [Online]. Available: https://api.semanticscholar.org/CorpusID:236088010
[23] N. Le, V. S. Rathour, K. Yamazaki, K. Luu, and M. Savvides, “Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey,” 2021. [Online]. Available: https://arxiv.org/abs/2108.11510

Details

Primary Language

English

Subjects

Materials Engineering (Other)

Journal Section

Research Article

Authors

İrem Çiçek
0009-0001-4932-8969
Türkiye

Nafiye Nur Apaydın
0009-0006-3438-7401
Türkiye

İrfan Kılıç ^*
0000-0001-5079-2825
Türkiye

Orhan Yaman
0000-0001-9623-2284
Türkiye

Mehmet Karaköse
0000-0002-3276-3788
Türkiye

Publication Date

June 29, 2026

Submission Date

May 14, 2026

Acceptance Date

June 29, 2026

Published in Issue

Year 2026 Volume: 9 Number: 1

DOI

https://doi.org/10.54565/jphcfum.1951468

IZ

https://izlik.org/JA66FG68BH

Cite

RIS / Bibtex

APA

Çiçek, İ., Apaydın, N. N., Kılıç, İ., Yaman, O., & Karaköse, M. (2026). Vehicle Color-Type Detection Based on YOLOv8 and K-Means. Journal of Physical Chemistry and Functional Materials, 9(1), 87-95. https://doi.org/10.54565/jphcfum.1951468

AMA

1.Çiçek İ, Apaydın NN, Kılıç İ, Yaman O, Karaköse M. Vehicle Color-Type Detection Based on YOLOv8 and K-Means. Journal of Physical Chemistry and Functional Materials. 2026;9(1):87-95. doi:10.54565/jphcfum.1951468

Chicago

Çiçek, İrem, Nafiye Nur Apaydın, İrfan Kılıç, Orhan Yaman, and Mehmet Karaköse. 2026. “Vehicle Color-Type Detection Based on YOLOv8 and K-Means”. Journal of Physical Chemistry and Functional Materials 9 (1): 87-95. https://doi.org/10.54565/jphcfum.1951468.

EndNote

Çiçek İ, Apaydın NN, Kılıç İ, Yaman O, Karaköse M (June 1, 2026) Vehicle Color-Type Detection Based on YOLOv8 and K-Means. Journal of Physical Chemistry and Functional Materials 9 1 87–95.

IEEE

[1]İ. Çiçek, N. N. Apaydın, İ. Kılıç, O. Yaman, and M. Karaköse, “Vehicle Color-Type Detection Based on YOLOv8 and K-Means”, Journal of Physical Chemistry and Functional Materials, vol. 9, no. 1, pp. 87–95, June 2026, doi: 10.54565/jphcfum.1951468.

ISNAD

Çiçek, İrem - Apaydın, Nafiye Nur - Kılıç, İrfan - Yaman, Orhan - Karaköse, Mehmet. “Vehicle Color-Type Detection Based on YOLOv8 and K-Means”. Journal of Physical Chemistry and Functional Materials 9/1 (June 1, 2026): 87-95. https://doi.org/10.54565/jphcfum.1951468.

JAMA

1.Çiçek İ, Apaydın NN, Kılıç İ, Yaman O, Karaköse M. Vehicle Color-Type Detection Based on YOLOv8 and K-Means. Journal of Physical Chemistry and Functional Materials. 2026;9:87–95.

MLA

Çiçek, İrem, et al. “Vehicle Color-Type Detection Based on YOLOv8 and K-Means”. Journal of Physical Chemistry and Functional Materials, vol. 9, no. 1, June 2026, pp. 87-95, doi:10.54565/jphcfum.1951468.

Vancouver

1.İrem Çiçek, Nafiye Nur Apaydın, İrfan Kılıç, Orhan Yaman, Mehmet Karaköse. Vehicle Color-Type Detection Based on YOLOv8 and K-Means. Journal of Physical Chemistry and Functional Materials. 2026 Jun. 1;9(1):87-95. doi:10.54565/jphcfum.1951468