Research on autonomous driving image recognition based on a new real-time object detection model YOLOv5st

Qingfeng Xue; Xinyu Wang; Gepeng Tu; Jing Wu

doi:10.1117/12.3009244

11 October 2023 Research on autonomous driving image recognition based on a new real-time object detection model YOLOv5st

Qingfeng Xue, Xinyu Wang, Gepeng Tu, Jing Wu

Author Affiliations +

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129182A (2023) https://doi.org/10.1117/12.3009244
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Considering the complexity of the driving environment and the high uncertainty of road conditions in autonomous driving, this paper proposes a real-time object detection model YOLOv5st, which is the integration of the advantages of YOLOv5s and Swin Transformer to achieve image recognition and object detection tasks in autonomous driving environment perception. The proposed model YOLOv5st is tested on the Transportation of Wuhan China (TOWC) traffic dataset, and the results show that the mean Average Precision (mAP) of the model is 89.8% in the validation set, which is a 2.0% improvement compared to YOLOv5s. And the average inference time of a single image of the new model is 20.6 ms, which can meet the real-time requirements. The proposed improved model has the characteristics of high accuracy, fast speed, and strong robustness, and is suitable for object detection tasks in autonomous driving vehicles. This study provides a new fusion algorithm for the perception of autonomous driving environments and provides new ideas and methods for the practical application of the algorithm through testing on the Wuhan traffic environment dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingfeng Xue, Xinyu Wang, Gepeng Tu, and Jing Wu "Research on autonomous driving image recognition based on a new real-time object detection model YOLOv5st", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129182A (11 October 2023); https://doi.org/10.1117/12.3009244

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KEYWORDS

Object detection

Education and training

Transformers

Autonomous driving

Convolution

Performance modeling

Data modeling

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