School of Computer Science and Technology, Hefei Normal University, Hefei 230601, Anhui, China.
Hefei Xinhuo Information Technology Co., Ltd., Hefei 230000, Anhui, China.
Comput Intell Neurosci. 2023 Jan 19;2023:7812276. doi: 10.1155/2023/7812276. eCollection 2023.
The classification and detection of traffic status plays a vital role in the urban smart transportation system. The classification and mastery of the traffic status at different time periods and sections will help the traffic management department to optimize road management and implement rescue in real time. Travelers can follow the traffic conditions. We choose the best route to effectively improve travel efficiency and safety. However, due to factors such as weather, time of day, lighting, and sample labeling costs, the existing classification methods are insufficient in real time and detection accuracy to meet application requirements. In order to solve this problem, this article aims to effectively transfer and apply the pretrained model learned on large-scale image data sets to small-sample road traffic data sets. By sharing common visual features, model weight parameter migration, and fine-tuning, the road is finally optimized. Traffic conditions classification is based on Traffic-Net. Experiments show that the method in this article can not only obtain a prediction accuracy of more than 96% but also can effectively reduce the model training time and meet the needs of practical applications.
交通状态的分类和检测在城市智能交通系统中起着至关重要的作用。对不同时间段和路段的交通状态进行分类和掌握，有助于交通管理部门优化道路管理，并实时实施救援。出行者可以根据交通状况选择最佳路线，有效提高出行效率和安全性。但是，由于天气、时间、照明和样本标记成本等因素的影响，现有的分类方法在实时性和检测精度方面都无法满足应用需求。为了解决这个问题，本文旨在有效地将在大规模图像数据集上学习到的预训练模型转移和应用于小样本道路交通数据集。通过共享通用的视觉特征、模型权重参数迁移和微调，最终优化道路。交通状况分类基于 Traffic-Net。实验表明，本文方法不仅可以获得超过 96%的预测精度，还可以有效地减少模型训练时间，满足实际应用的需求。
