计算机科学 ›› 2019, Vol. 46 ›› Issue (1): 303-308.doi: 10.11896／j.issn.1002-137X.2019.01.047
何霞, 汤一平, 袁公萍, 陈朋, 王丽冉
HE Xia, TANG Yi-ping, YUAN Gong-ping, CHEN Peng, WANG Li-ran
摘要： 针对在将卡口非结构化视频图像数据转化为智能结构化信息的过程中存在环境的复杂性、需求的多样性、任务的关联性和识别的实时性等问题,提出了一种级联多任务深度学习网络的卡口识别引擎方法,其通过充分利用分割、检测、识别等任务之间的相互联系实现了高精度的、高效的、同步实时的卡口车辆多种基本信息的识别(车型、品牌、车系、车身颜色以及车牌等识别任务)。首先,利用深度卷积神经网络自动完成车型的深度特征学习,在特征图上进行逻辑回归,从卡口道路复杂背景中提取出感兴趣区域(包括多车辆对象);然后,利用多任务深度学习网络对提取出来的车辆对象实现多层次的多任务识别。实验结果表明,提出的方法在识别精度和效率上都明显优于传统计算机视觉方法和现有的基于深度学习的识别引擎技术,该方法对车型、品牌、车系及车牌的识别与检测精度均达到98%以上,检测效率提升了1.6倍。
中图分类号:
| [1]GU R S.Research on vehicle detection and identification for video at traffic checkpoints [D].Nanjing:Nanjing University,2014:11.(in Chinese)<br /> 顾人舒.交通卡口车辆检测与自动识别技术研究[D].南京:南京大学,2014:11.<br /> [2]GUPTE S,MASOUD O,MARTIN R F K,et al.Detection and classification of vehicles [J].Intelligent Transportation Systems,2002,3(1):37-47.<br /> [3]JIANG M,LI H.Vehicle classification based on hierarchical support vector machine [M]//Computer Engineering and Networking.Berlin:Springer International Publishing,2014:593-600.<br /> [4]LOWE D G.Distinctive Image Features from Scale-Invariant Keypoints [J].International Journal of Computer Vision,2004,60(2):91-110.<br /> [5]NEGRI P,CLADY X,MILGRAM M,et al.An oriented-contour point based voting algorithm for vehicle type classification [C]//Proceedings of IEEE International Conference on Pattern Re-cognition.New York:IEEE Press,2006:574-577.<br /> [6]GE F X,SHI Y,SUN B,et al.Sparse representation based classification by using PCA-SIFT descriptors [C]//Proceedings of IEEE International Conference on Information Science and Technology.New York:IEEE Press,2014:429-432.<br /> [7]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation [C]//Proceedings of the IEEE conference on computer vision and pattern recognition.New York:IEEE Press,2014:580-587.<br /> [8]GIRSHICK R.Fast R-CNN [C]//Proceedings of the IEEE International Conference on Computer Vision.New York:IEEE Press,2015:1440-1448.<br /> [9]REN S Q,HE K M,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks [C]//Proceedings of the IEEE International Advances on Neural Information Processing Systems.New York:IEEE Press,2015:91-99.<br /> [10]HE T,HUANG W,QIAO Y,et al.Text-Attentional convolutional neural network for scene text detection [J].IEEE Transactions on Image Processing,2016,25(6):2529-2541.<br /> [11]ZHANG Z,LUO P,CHEN C L,et al.Facial Landmark Detection by Deep Multi-task Learning [C]//Proceedings of the IEEE International Conference on European Conference on Computer Vision.New York:IEEE Press,2014:94-108.<br /> [12]HOOCHANG S,ROTH H R,GAO M,et al.Deep convolutional<br /> neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning [J].IEEE Transactions on Medical Imaging,2016,35(5):1285-1298.<br /> [13]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks [C]//Proceedings of the IEEE International Conference on Neural Information Processing Systems.New York:IEEE Press,2012:1097-1105.<br /> [14]ZHEN D,WU,Y W,PEI M T,et al.Vehicle Type Classification Using a Semisupervised Convolutional Neural Network [J].Intelligent Transportation Systems IEEE Transactions,2015,16(4):2247-2256.<br /> [15]UIJLINGS J R R,SANDE K E A V D,GEVERS T,et al.Selective Search for Object Recognition [J].International Journal of Computer Vision,2013,104(2):154-171.<br /> [16]DOLLAR P,ZITNICK C L.Structured forests for fast edge detection [C]//Proceedings of the IEEE International Conference on Computer Vision.New York:IEEE Press,,2013:1841-1848.<br /> [17]YANG L,LUO P,CHEN C L,et al.A large-scale car dataset for fine-grained categorization and verification [C]//Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015:3973-3981. |
| [1] | 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 |
| [2] | 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 |
| [3] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [4] | 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 |
| [5] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
| [6] | 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 |
| [7] | 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 |
| [8] | 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 |
| [9] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
| [10] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [11] | 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 |
| [12] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [13] | 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 |
| [14] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
| [15] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
|
||
首页