计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210600142-7.doi: 10.11896/jsjkx.210600142
顾曦龙, 宫宁生, 胡乾生
GU Xi-long, GONG Ning-sheng, HU Qian-sheng
摘要： 为了能快速、有效地识别视频中的车辆信息,文中结合YOLOv3算法和CNN算法的优点,设计了一种能实时识别车辆多标签信息的算法。首先,利用具有较高识别速度和准确率的YOLOv3实现对视频流中车辆的实时监测和定位。在获得车辆的位置信息后,再将车辆信息传入经过简化与优化的类VGGNet多标签分类网络中,对车辆进行多标签标识。最后将标签信息输出至视频流,得到对视频中车辆的实时多标签识别。文中训练与测试数据集来源为KITTI数据集和通过Bing Image Search API获取的多标签数据集。实验结果证明,所提方法在KITTI数据集上的mAP达到了91.27,多标签平均准确率达到80%以上,视频帧率达到35 fps,在保证实时性的基础上取得了较好的车辆识别和多标签分类效果。
中图分类号:
| [1]ZHANG Q,LI J F,ZHUO L.Review of Vehicle RecognitionTechnology[J].Journal of Beijing University of Technology,2018,44(3):382-392. [2]BAEK N,PARK S M,KIM K J,et al.Vehicle color classification based on the support vector machine method[C]//International Conference on Intelligent Computing(ICIC).2007:1133-1139. [3]LI L J,SU H,XING E P,et al.Object bank:a high-level image representation for scene classification & semantic feature sparsification[C]//Advances in Neural Information Processing Systems(ANIPS).2010:1378-1386. [4]BUCH N,ORWELL J,VELASTIN S A.Detection and classification of vehicles for urban traffic scenes[C]//Visual Information Engineering(VIE).2008:182-187. [5]RACHMADI R F,PURNAMA I.Vehicle color recognitionusing convolutional neural network[J].arXiv,2015:1510.07391. [6]KRAUSE J,JIN H,YANG J,et al.Fine-grained recognitionwithout part annotations[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition(CVPR).2015:5546-5555. [7]HU A,LI H,ZHANG F,et al.Deep Boltzmann machines based vehicle recognition[C]//The 26th Chinese Control and Decision Conference(CCDC).2014:3033-3038. [8]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].International Conference on Neural Information Processing Systems,2017,37(6):1137-1149. [9]DAI J,LI Y,HE K,et al.R-FCN:Object Detection via Region-based Fully Convolutional Networks[C]//Proceedings of the 30th International Conference on Neural Information Processing System(NIPS).2016:379-387. [10]REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:779-788. [11]LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single ShotMultiBox Detector[C]//European Conference on Computer Vision(ECCV).2016:21-38. [12]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2014:580-587. [13]HE K,ZHANG X,REN S,et al.Spatial Pyramid Pooling inDeep Convolutional Networks for Visual Recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,37(9):1904-16. [14]REDMON J,FARHADIA.YOLO9000:Better,Faster,Stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2017:6517-6525. [15]REDMON J,FARHADI A.YOLOv3:An Incremental Improvement[J].arXiv:1804.02767,2018. [16]ZHANG Z,QIAO S,XIE C,et al.Single-Shot Object Detection with Enriched Semantics[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2018:5813-5821. [17]ZHANG S,WEN L,BIAN X,et al.Single-Shot RefinementNeural Network for Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2018,4203-4212. [18]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016,770-778. [19]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition [J].arXiv:1409.1556,2014. [20]SRIVASTAVA N,HINTON G,KRIZHEVSKYA,et al.Drop-out:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958. [21]GEIGER A,LENZ P,STILLER C,et al.Vision meets robotics:The KITTI dataset[J].The International Journal of Robotics Research,2013,32(11):1231-1237. |
| [1] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
| [2] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
| [4] | 刘冬梅, 徐洋, 吴泽彬, 刘倩, 宋斌, 韦志辉. 基于边框距离度量的增量目标检测方法 Incremental Object Detection Method Based on Border Distance Measurement 计算机科学, 2022, 49(8): 136-142. https://doi.org/10.11896/jsjkx.220100132 |
| [5] | 王灿, 刘永坚, 解庆, 马艳春. 基于软标签和样本权重优化的Anchor Free目标检测算法 Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization 计算机科学, 2022, 49(8): 157-164. https://doi.org/10.11896/jsjkx.210600240 |
| [6] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [7] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
| [8] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [9] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [10] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [11] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
| [12] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
| [13] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
| [14] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
| [15] | 刘伟业, 鲁慧民, 李玉鹏, 马宁. 指静脉识别技术研究综述 Survey on Finger Vein Recognition Research 计算机科学, 2022, 49(6A): 1-11. https://doi.org/10.11896/jsjkx.210400056 |
|
||
首页