基于改进YOLOv2算法的交通标志检测

针对YOLOv2算法实际检测到的小尺寸交通标志质量不佳, 识别率低, 实时性差的问题, 提出一种基于改进YOLOv2的交通标志检测方法. 首先, 通过直方图均衡化、BM3D对图像增强以获取高质量图像; 接着, 将网络顶层卷积层输出的特征图进行精细划分, 得到高细粒度的特征图, 以检测高质量、小尺寸的交通标志; 最后, 采用归一化及优化置信度评分比例对损失函数进行改进. 在结合CCTSD (中国交通标志检测数据集)和TT100K数据集的新数据集上进行实验, 与YOLOv2网络模型相比, 经过改进后的网络识别率提高了8.7%, 同时模型的识别速度提高了15 FPS. 实验结果表明: 所提方法能够对小尺寸交通标志进行精准检测.

Traffic Sign Recognition Based on Improved YOLOv2 Algorithm

The small-sized traffic signs actually detected by the YOLOv2 algorithm are of poor quality, low recognition rate, and poor real-time performance. This study proposes a traffic sign detection method based on improved YOLOv2. Firstly, the image is enhanced by histogram equalization and BM3D method, with high-quality images. Moreover, the top-level convolutional layer output feature map of the network is finely divided to obtain fine-grained feature maps to detect high-quality, small-sized traffic signs. Finally, the loss function is improved by normalization and optimization of the confidence score ratio method. Experiments were carried out on a new data set combining CCTSD (China Traffic Sign Detection Dataset) and TT100K dataset. Compared with the YOLOv2 network model, the network recognition rate increases by 8.7% and the recognition speed of the model is improved by 15 FPS. Experimental results show that small-sized traffic signs can be accurately detected by proposed method.