基于改进YOLOv2算法的交通标志检测

Traffic Sign Recognition Based on Improved YOLOv2 Algorithm

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.

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 BM3 D method,with high-quality images.Moreover,the toplevel 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 TT100 K 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 smallsized traffic signs can be accurately detected by proposed method.