Luo Ruikang, Song Yaofeng, Ye Longfei, Su Rong
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel). 2024 Nov 29;24(23):7662. doi: 10.3390/s24237662.
Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog.
准确的车辆类型分类在智能交通系统中起着重要作用。了解道路状况至关重要，并且通常有助于交通信号灯控制系统做出相应反应以缓解交通拥堵。诸如航拍照片和遥感数据等新技术和综合数据源提供了更丰富、更高维度的信息。此外，由于深度神经网络技术的快速发展，基于图像的车辆分类方法在处理数据时能够更好地提取潜在的客观特征。最近，已经提出了几种深度学习模型来解决这个问题。然而，传统的纯卷积方法在全局信息提取方面存在局限性，恶劣天气等复杂环境严重限制了它们的识别能力。为了提高复杂环境下的车辆类型分类能力，本研究提出了一种新颖的密集连接卷积-Transformer-in-Transformer神经网络（Dense-TNT）框架，通过堆叠密集连接卷积网络（DenseNet）和Transformer-in-Transformer（TNT）层进行车辆类型分类。部署了四个不同天气条件下三个区域的车辆数据来评估识别能力。我们的实验结果验证了所提出的车辆分类模型的识别能力，表明即使在大雾天气下识别能力也几乎没有下降。
