Iranian Vehicle Images Dataset for Object Detection Algorithm

Maleki, Pouria; Ramazani, Abbas; Khotanlou, Hassan; Ojaghi, Sina

doi:10.22044/jadm.2024.13858.2501

Document Type : Original/Review Paper

Authors

¹ Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.

² Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.

³ School of Computer and Electrical Engineering, University of Tehran, Tehran, Iran.

https://doi.org/10.22044/jadm.2024.13858.2501

Abstract

Providing a dataset with a suitable volume and high accuracy for training deep neural networks is considered to be one of the basic requirements in that a suitable dataset in terms of the number and quality of images and labeling accuracy can have a great impact on the output accuracy of the trained network. The dataset presented in this article contains 3000 images downloaded from online Iranian car sales companies, including Divar and Bama sites, which are manually labeled in three classes: car, truck, and bus. The labels are in the form of 5765 bounding boxes, which characterize the vehicles in the image with high accuracy, ultimately resulting in a unique dataset that is made available for public use.The YOLOv8s algorithm, trained on this dataset, achieves an impressive final precision of 91.7% for validation images. The Mean Average Precision (mAP) at a 50% threshold is recorded at 92.6%. This precision is considered suitable for city vehicle detection networks. Notably, when comparing the YOLOv8s algorithm trained with this dataset to YOLOv8s trained with the COCO dataset, there is a remarkable 10% increase in mAP at 50% and an approximately 22% improvement in the mAP range of 50% to 95%.

Keywords

Main Subjects

H.5. Image Processing and Computer Vision

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Iranian Vehicle Images Dataset for Object Detection Algorithm

References

References

Volume 12, Issue 1January 2024Pages 127-136

Volume 12, Issue 1
January 2024
Pages 127-136