Lai Huaqing, Chen Liangyan, Liu Weihua, Yan Zi, Ye Sheng
School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
Sensors (Basel). 2023 Jun 3;23(11):5307. doi: 10.3390/s23115307.
The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving. To address this issue, a new traffic sign dataset, namely the enhanced Tsinghua-Tencent 100K (TT100K) dataset, was constructed, which includes the number of difficult samples generated using various data augmentation strategies such as fog, snow, noise, occlusion, and blur. Meanwhile, a small traffic sign detection network for complex environments based on the framework of YOLOv5 (STC-YOLO) was constructed to be suitable for complex scenes. In this network, the down-sampling multiple was adjusted, and a small object detection layer was adopted to obtain and transmit richer and more discriminative small object features. Then, a feature extraction module combining a convolutional neural network (CNN) and multi-head attention was designed to break the limitations of ordinary convolution extraction to obtain a larger receptive field. Finally, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to make up for the sensitivity of the intersection over union (IoU) loss to the location deviation of tiny objects in the regression loss function. A more accurate size of the anchor boxes for small objects was achieved using the K-means++ clustering algorithm. Experiments on 45 types of sign detection results on the enhanced TT100K dataset showed that the STC-YOLO algorithm outperformed YOLOv5 by 9.3% in the mean average precision (mAP), and the performance of STC-YOLO was comparable with that of the state-of-the-art methods on the public TT100K dataset and CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB2021) dataset.
交通标志的检测很容易受到天气变化、部分遮挡和光照强度的影响，这增加了自动驾驶实际应用中的潜在安全隐患数量。为了解决这个问题，构建了一个新的交通标志数据集，即增强型清华-腾讯100K（TT100K）数据集，其中包括使用雾、雪、噪声、遮挡和模糊等各种数据增强策略生成的困难样本数量。同时，基于YOLOv5框架构建了一个适用于复杂环境的小型交通标志检测网络（STC-YOLO）。在这个网络中，调整了下采样倍数，并采用了一个小目标检测层来获取和传输更丰富、更具判别力的小目标特征。然后，设计了一个结合卷积神经网络（CNN）和多头注意力的特征提取模块，以打破普通卷积提取的局限性，从而获得更大的感受野。最后，引入了归一化高斯瓦瑟斯坦距离（NWD）度量，以弥补交并比（IoU）损失在回归损失函数中对微小目标位置偏差的敏感性。使用K-means++聚类算法获得了更精确的小目标锚框尺寸。在增强型TT100K数据集上对45种标志检测结果进行的实验表明，STC-YOLO算法在平均精度均值（mAP）上比YOLOv5高出9.3%，并且STC-YOLO的性能与公共TT100K数据集和中北大学中文交通标志检测基准（CCTSDB2021）数据集上的最先进方法相当。
