College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
Cai Ji Center Primary School, Suqian 223813, Jiangsu, China.
Comput Intell Neurosci. 2022 Aug 3;2022:2682921. doi: 10.1155/2022/2682921. eCollection 2022.
With the rapid development of intelligent transportation, more and more vehicles are equipped with intelligent traffic sign recognition systems, which can reduce the potential safety hazards caused by human cognitive errors. Therefore, a more safe and reliable traffic sign recognition system is the demand of drivers, and it is also the research hotspot of current automobile manufacturers. However, the pictures taken by the actual driving car are inevitably distorted and blurred. In addition, there are external uncontrollable factors, such as the impact of bad weather, which make the research of traffic sign recognition system face many difficulties, and the practical application is far from mature. In order to solve the above challenges, this paper proposes a Yolo model for traffic sign recognition. Firstly, the traffic signs are roughly divided into several categories and then preprocessed according to the characteristics of various types of signs. The processed pictures are input into the optimized convolutional neural network to subdivide the categories to obtain the specific categories. Finally, the proposed recognition algorithm is tested with the data set based on the German traffic sign recognition standard and compared with other baseline algorithms. The results show that the algorithm greatly improves the running speed on the basis of ensuring a high classification accuracy and is more suitable for traffic sign recognition system.
随着智能交通的快速发展，越来越多的车辆配备了智能交通标志识别系统，这可以减少人为认知错误带来的潜在安全隐患。因此，一个更安全、更可靠的交通标志识别系统是驾驶员的需求，也是当前汽车制造商的研究热点。然而，实际行驶中的汽车拍摄的图片不可避免地会有扭曲和模糊。此外，还有外部不可控因素，如恶劣天气的影响，这使得交通标志识别系统的研究面临许多困难，实际应用还远未成熟。为了解决上述挑战，本文提出了一种用于交通标志识别的 Yolo 模型。首先，根据各种类型标志的特点将交通标志大致分为几类，然后进行预处理。将处理后的图片输入到优化后的卷积神经网络中，对类别进行细分，得到具体的类别。最后，基于德国交通标志识别标准的数据集对提出的识别算法进行测试，并与其他基线算法进行比较。结果表明，该算法在保证高分类精度的基础上大大提高了运行速度，更适合交通标志识别系统。
