Alawaji Khaldaa, Hedjar Ramdane, Zuair Mansour
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel). 2024 May 21;24(11):3282. doi: 10.3390/s24113282.
Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural networks, have shown exceptional performance in various computer vision applications. The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer vision and deep learning techniques. Previous work has focused on symbol-based traffic signals, where popular single-task learning models have been trained and tested. Therefore, several comparisons have been conducted to select accurate single-task learning models. For further improvement, these models are employed in a multi-task learning approach. Indeed, multi-task learning algorithms are built by sharing the convolutional layer parameters between the different tasks. Hence, for the multi-task learning approach, different experiments have been carried out using pre-trained architectures like, for instance, InceptionResNetV2 and DenseNet201. A range of traffic signs and traffic lights are employed to validate the designed model. An accuracy of 99.07% is achieved when the entire network has been trained. To further enhance the accuracy of the model for traffic signs obtained from the street, a region of interest module is added to the multi-task learning module to accurately extract the traffic signs available in the image. To check the effectiveness of the adopted methodology, the designed model has been successfully tested in real-time on a few Riyadh highways.
在未来几年里，设计用于固定路线的人员和货物无人驾驶运输系统的进步将彻底改变运输系统。因此，对于安全的运输系统而言，基于计算机视觉检测和识别交通信号变得越来越重要。深度学习方法，尤其是卷积神经网络，在各种计算机视觉应用中都表现出了卓越的性能。本研究的目标是使用计算机视觉和深度学习技术精确检测和识别街道上的交通标志。先前的工作主要集中在基于符号的交通信号上，在那里流行的单任务学习模型已经得到训练和测试。因此，已经进行了几次比较以选择准确的单任务学习模型。为了进一步改进，这些模型被应用于多任务学习方法中。实际上，多任务学习算法是通过在不同任务之间共享卷积层参数构建的。因此，对于多任务学习方法，已经使用诸如InceptionResNetV2和DenseNet201等预训练架构进行了不同的实验。使用一系列交通标志和交通灯来验证所设计的模型。当整个网络都经过训练时，准确率达到了99.07%。为了进一步提高从街道获取的交通标志模型的准确率，在多任务学习模块中添加了一个感兴趣区域模块，以准确提取图像中可用的交通标志。为了检验所采用方法的有效性，所设计的模型已经在利雅得的几条高速公路上成功进行了实时测试。
