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计算机系统应用:2020,29(6):155-162
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基于改进YOLOv2算法的交通标志检测
(西安科技大学 机械工程学院, 西安 710054)
Traffic Sign Recognition Based on Improved YOLOv2 Algorithm
(College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)
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投稿时间:2019-10-14    修订日期:2019-11-07
中文摘要: 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.
中文关键词: 无人驾驶  交通标志检测  YOLOv2  BM3D  损失函数
Abstract: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.
文章编号:7459     中图分类号:    文献标志码:
基金项目:国家自然科学基金(51974229, 51805428); 陕西省自然科学基础研究计划(20018JQ5205)
引用文本:
张传伟,李妞妞,岳向阳,杨满芝,王睿,丁宇鹏.基于改进YOLOv2算法的交通标志检测.计算机系统应用,2020,29(6):155-162
ZHANG Chuan-Wei,LI Niu-Niu,YUE Xiang-Yang,YANG Man-Zhi,WANG Rui,DING Yu-Peng.Traffic Sign Recognition Based on Improved YOLOv2 Algorithm.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):155-162

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