Real-World Traffic Detection: Achieving High Accuracy Using Deep Learning Based YOLOv5 and YOLOv8 Architectures
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
In numerous nations, the imperative role of traffic monitoring systems is essential for overseeing and controlling vehicular and pedestrian traffic. In recent years, various techniques have been presented for automated detection to optimize traffic. The methods presented in the literature have their own pros and cons. This paper proposes two different traffic detection models using YOLOv5 and YOLOv8. In addition, this paper proposes an efficient data pre-processing algorithm to achieve better accuracy for detecting various classes of vehicles in the traffic including pedestrians. An efficient loss optimization strategy is proposed and adopted while training the model to reduce the training loss. This paper discusses the choice between two deep learning models, YOLOv5 and YOLOv8, for identifying different types of objects of interest on the road in urban areas. The efficiency of the proposed models is evaluated in this paper using multiple performance metrics, including their accuracy. The comparative analysis of the proposed models with existing models indicate that the proposed models are on par in terms of accuracy with existing strategies while integrated with the additional complexity of pedestrian detection.
Identifier
85200585836 (Scopus)
ISBN
[9798350384895]
Publication Title
Proceedings - IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2024
External Full Text Location
https://doi.org/10.1109/IC_ASET61847.2024.10596169
Recommended Citation
Pavithran, Rahul; Thakkar, Jinal Jagdishkumar; Shirke, Sunit Sanjay; Masapalli, Vaishnavi; and Kaur, Arashdeep, "Real-World Traffic Detection: Achieving High Accuracy Using Deep Learning Based YOLOv5 and YOLOv8 Architectures" (2024). Faculty Publications. 925.
https://digitalcommons.njit.edu/fac_pubs/925
