Abstract:

In order to improve the recognition accuracy and detection speed of traffic participants by intelligent networked vehicles and traffic monitoring systems so that they can timely respond to the safety hazards in the mixed traffic environment in urban space, a mixed traffic participant detection model in urban space based on the improved YOLOv8n algorithm was proposed. Firstly, geometric transformation and pixel transformation enhancement strategies were employed in the data input stage to prevent overfitting and improve robustness, and generalization. Secondly, the SPD-Conv module was used to replace all original convolution layers of the YOLOv8n algorithm, which enhances the feature extraction capability for low-resolution small targets. Meanwhile, the CA module was added to the fusion structure of the neck network of the YOLOv8n algorithm to improve the recognition accuracy of key information with almost no additional computational overhead. Then, the boundary box loss function EIoU was used to replace the original loss function, enabling the model to achieve superior convergence speed and recognition stability. Finally, the ablation and comparison experiments were carried out with the public and self-built integrated traffic participant dataset, and the real-time detection experiment was carried out with the automatic driving experiment platform. The experimental results show that compared to the YOLOv8n model, the improved SEC-YOLO model has increased mAP and FPS by 3.2% and 7.9% respectively. The SEC-YOLO model outperforms mainstream models in terms of mAP and FPS as well. The average accuracy of real-scene detection on the automatic driving experimental platform is around 95%. The SEC-YOLO algorithm model achieves higher detection accuracy for urban traffic participants, with stronger robustness and real-time performance.

Key words: YOLOv8n, spatial mixing, traffic participants, detection model, space-to-depth convolution (SPD-Conv), coordinate attention (CA)