基于加权密集连接卷积网络的快速交通标志检测

为提高算法对交通标志快速定位的准确性，改善现有检测算法在复杂交通环境下检测效果不佳、实时性较差的问题，提出一种基于动态加权密集连接卷积网络的交通标志快速检测算法. 选用YOLOv2 作为基础网络，通过增加动态加权密集块对各层特征图的权重进行调节，实现深层高语义信息和浅层低语义信息的融合；使用MobileNet 轻量化网络结构，通过可分离卷积操作有效降低网络的计算成本；针对池化操作中图像特征丢失严重问题引入 CBAM模块，利用通道注意力和空间注意力信息增强关键特征的表达能力. 实验结果表明，本文算法在GTSDB数据集上分别达到了96.14%的检测精度和139 frame/s 的检测速度，在保证较高检测精度的同时，能够有效提高检测效率，满足实时检测要求.

Fast Traffic Sign Detection Based on Weighted Densely Connected Convolutional Network

The current traffic sign detection algorithms appear to have poor detection effect and low practicability under complex traffic environments. This paper proposed a fast traffic sign detection algorithm based on dynamic weighted densely connected convolutional network. The purpose is to enhance the detection accuracy of the algorithm and identify traffic signs with improved speed. YOLOv2 was selected as the basic network, and the weight of each layer's feature map was adjusted by adding dynamic weighted densely blocks. The deep highsemantic information and shallow low-semantic information was integrated subsequently. The lightweight network structure in MobileNet and the separable convolution operation reduced the network computing costs effectively. To resolve the image feature loss problem in pooling operation, the study used the Convolutional Block Attention Module (CBAM) to enhance the performance of key features by using channel attention and spatial attention information. The experimental results confirmed that the proposed algorithm achieved a detection accuracy of 96.14%, and the detection speed was 139 frame/s on the German Traffic Sign Detection Benchmark (GTSDB) dataset. The algorithm effectively improved the detection efficiency and practicability, and maintained good detection accuracy.