Zhang Jing, Zhang Haoliang, Lang Ding, Xu Yuguang, Li Hong-An, Li Xuewen
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.
College of Energy, Xi'an University of Science and Technology, Xi'an 710054, China.
Math Biosci Eng. 2023 May 18;20(7):12240-12262. doi: 10.3934/mbe.2023545.
The recognition of traffic signs is of great significance to intelligent driving and traffic systems. Most current traffic sign recognition algorithms do not consider the impact of rainy weather. The rain marks will obscure the recognition target in the image, which will lead to the performance degradation of the algorithm, a problem that has yet to be solved. In order to improve the accuracy of traffic sign recognition in rainy weather, we propose a rainy traffic sign recognition algorithm. The algorithm in this paper includes two modules. First, we propose an image deraining algorithm based on the Progressive multi-scale residual network (PMRNet), which uses a multi-scale residual structure to extract features of different scales, so as to improve the utilization rate of the algorithm for information, combined with the Convolutional long-short term memory (ConvLSTM) network to enhance the algorithm's ability to extract rain mark features. Second, we use the CoT-YOLOv5 algorithm to recognize traffic signs on the recovered images. In this paper, in order to improve the performance of YOLOv5 (You-Only-Look-Once, YOLO), the 3 × 3 convolution in the feature extraction module is replaced by the Contextual Transformer (CoT) module to make up for the lack of global modeling capability of Convolutional Neural Network (CNN), thus improving the recognition accuracy. The experimental results show that the deraining algorithm based on PMRNet can effectively remove rain marks, and the evaluation indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are better than the other representative algorithms. The mean Average Precision (mAP) of the CoT-YOLOv5 algorithm on the TT100k datasets reaches 92.1%, which is 5% higher than the original YOLOv5.
交通标志的识别对于智能驾驶和交通系统具有重要意义。当前大多数交通标志识别算法没有考虑雨天天气的影响。雨痕会遮挡图像中的识别目标，导致算法性能下降，这一问题尚未得到解决。为了提高雨天天气下交通标志识别的准确性，我们提出了一种雨天交通标志识别算法。本文的算法包括两个模块。首先，我们提出了一种基于渐进式多尺度残差网络（PMRNet）的图像去雨算法，该算法使用多尺度残差结构来提取不同尺度的特征，从而提高算法对信息的利用率，并结合卷积长短期记忆（ConvLSTM）网络来增强算法提取雨痕特征的能力。其次，我们使用CoT-YOLOv5算法对恢复后的图像上的交通标志进行识别。在本文中，为了提高YOLOv5（You-Only-Look-Once，YOLO）的性能，将特征提取模块中的3×3卷积替换为上下文变换器（CoT）模块，以弥补卷积神经网络（CNN）缺乏全局建模能力的不足，从而提高识别准确率。实验结果表明，基于PMRNet的去雨算法能够有效去除雨痕，峰值信噪比（PSNR）和结构相似性指数测量（SSIM）等评价指标优于其他代表性算法。CoT-YOLOv5算法在TT100k数据集上的平均精度均值（mAP）达到92.1%，比原始的YOLOv5高5%。
