Object detection in urban traffic scenarios based on improved YOLOv8 model

Jiaqi Lu; Jie Yang

doi:10.1117/12.3034804

11 July 2024 Object detection in urban traffic scenarios based on improved YOLOv8 model

Jiaqi Lu, Jie Yang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101J (2024) https://doi.org/10.1117/12.3034804
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Object detection in urban traffic scenarios is a great challenge in computer vision due of the factors like occlusion, illumination, small target size and complex background. To enhance the accuracy of object detection in urban traffic scenarios, several innovations is applied to original YOLOv8. First, a novel C2f module embedded with deformable convolution network (DCN) is proposed to increase model's receptive field and therefore strengthen the model's robustness. Then, channel priority convolution attention (CPCA) mechanism is introduced to extract important features and thus enhance the regression and localization capability of the model. Furthermore, the loss function CIoU is replaced by ECIoU to improve detection frame positioning accuracy and convergence speed. From the experimental results on the VisDrone2019 dataset, the improved YOLOv8 model has obtained better performance than original YOLO models. This study provides a theoretical basis for more accurate object detection in complex urban traffic scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaqi Lu and Jie Yang "Object detection in urban traffic scenarios based on improved YOLOv8 model", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101J (11 July 2024); https://doi.org/10.1117/12.3034804

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KEYWORDS

Object detection

Data modeling

Performance modeling

Convolution

Education and training

Deformation

Feature extraction

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