| 融合SPP和改进FPN的YOLOv3交通标志检测 |
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| 引用本文： | 刘紫燕,袁磊,朱明成,马珊珊,陈霖周廷. 融合SPP和改进FPN的YOLOv3交通标志检测[J]. 计算机工程与应用, 2021, 57(7): 164-170. DOI: 10.3778/j.issn.1002-8331.2007-0117 |
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| 作者姓名： | 刘紫燕 袁磊 朱明成 马珊珊 陈霖周廷 |
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| 作者单位： | 1.贵州大学 大数据与信息工程学院,贵阳 550025 2.贵州理工学院 航空航天工程学院,贵阳 550003 |
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| 基金项目： | 贵州省科技计划项目;贵州省科学技术基金;贵州大学2017年度学术新苗培养及创新探索专项;贵州省教育厅创新群体重大研究项目;贵州省科技计划重点项目;贵州省普通高等学校工程研究中心项目;贵州省联合资金资助项目 |
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| 摘 要： | 针对交通标志目标检测尺寸较小、分辨率低、特征不明显问题,提出一种改进的YOLOv3网络模型.在利用颜色增强方法对交通标志进行数据增强后,改进原网络中的FPN结构,保留原网络中52×52的大尺度预测,然后利用YOLOv3网络中第二次下采样输出的特征图建立108×108的更大尺度预测.为了解决图像尺寸和失真的问题,在检测层...
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| 关 键 词： | 目标检测 交通标志 YOLOv3 数据增强 大尺度预测 |
YOLOv3 Traffic sign Detection based on SPP and Improved FPN |
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| LIU Ziyan,YUAN Lei,ZHU Mingcheng,MA Shanshan,CHEN Linzhouting. YOLOv3 Traffic sign Detection based on SPP and Improved FPN[J]. Computer Engineering and Applications, 2021, 57(7): 164-170. DOI: 10.3778/j.issn.1002-8331.2007-0117 |
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| Authors: | LIU Ziyan YUAN Lei ZHU Mingcheng MA Shanshan CHEN Linzhouting |
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| Affiliation: | 1.College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China2.School of Aerospace Engineering, Guizhou Institute of Technology, Guiyang 550003, China |
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| Abstract: | Aiming at solving the problems of small size, low resolution and insignificant features in traffic sign targets detection, an improved network model of YOLOv3 is proposed. After using the color enhancement method to enhance the traffic sign data, the FPN structure in the original network is improved, retaining the large-scale prediction with a scale of 52×52 in the original network, then it builds a larger-scale prediction with a scale of 108×108 by using the feature map of the second down-sampling output in the YOLOv3 network. In order to solve the problem of image size and distortion, it uses pooling operations with fixed block sizes of 5, 9, and 13 before the detection layer. And then, the separately output features are merged with the original feature map to achieve the same output size for different input sizes. Finally, the K-means clustering algorithm is used to cluster the TT100K traffic sign data set, the initial candidate box of the network is redefined, and the YOLOv3 network model, the improved YOLOv3 network model and other small target detection algorithms are used to compare experiments on the TT100K data set. The results show that the improved YOLOv3 network model can detect traffic signs more effectively, and the average detection accuracy of the improved YOLOv3 network model is 8.3%, 6.1% and 4.3% higher than that of the original YOLOv3 network model at three scales. When the FPS changes little, the recall rate and accuracy are significantly improved. At the same time, the improved YOLOv3 algorithm has better detection accuracy and real-time performance than other small target detection algorithms. |
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| Keywords: | object detection traffic sign YOLOv3 data enhancement large-scale prediction |
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