Vehicle Models Recognition for Intelligent Control of Vehicle Traffic by Deep Learning

Document Type : Original Article

Authors

1 MSc. student Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

2 Assistant Professor Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

3 Associate Professor Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

10.22034/abmir.2025.22898.1112

Abstract

With the increasing number of vehicles worldwide and the advancement of technology, intelligent transportation systems have emerged as a key solution for traffic control and road safety enhancement. One of the crucial components of these systems is vehicle type recognition. In this paper, a combination of EfficientNet-B0 and YOLO-V11 models is used for vehicle type classification. In this model, EfficientNet-B0 serves as the backbone of YOLO-V11, extracting image features and feeding them into the YOLO model to accurately determine the location and type of vehicles. To evaluate the performance of the proposed model, the BVMMR image dataset—containing over 5,000 of various Iranian vehicles—was utilized. The model was implemented using Python and executed in the Colab online environment. The results demonstrate that the proposed model achieves a mean Average Precision at 50% IoU (mAP50) of 99.3% and a mean Average Precision across IoU thresholds from 50% to 95% (mAP50-95) of 98.3%. Additionally, in terms of processing speed, the model delivers an efficient performance with an images average preprocessing time of 0.2 milliseconds, inference time of 2.8 milliseconds, and post-processing time of 2.5 milliseconds per image.

Keywords

Main Subjects

References

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Applied and basic Machine intelligence research
Volume 2, Issue 2
March 2025
Pages 111-124

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