Traffic Condition Classification Model Based on Traffic‐Net

Abstract

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.

Res2Net-based multi-scale and multi-attention model for traffic scene image classification

“…Robles-Serrano et al [ 44 ] extracted keyframes from traffic videos and employed a modified Inception V4 architecture to extract visual feature vectors for real-time classification and detection of traffic accidents. Furthermore [ 45 ], demonstrated the effectiveness of transfer learning using VGG16, GoogLeNet, and ResNet50 for classification tasks on the Traffic-Net dataset, achieving an accuracy of over 95%.…”