计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 309-314.
曲佳博, 秦勃
QU Jia-bo, QIN Bo
摘要: 深度学习是基于图像的交通标志检测和识别处理的研究热点,已取得了显著的效果。针对基于车载视频的交通标志检测和识别处理问题,文中根据图像序列的帧间时空连续关系构建了时空关系模型(Spatiotemporal Model,STM),并与多尺度卷积神经网络(Convolutional Neural Networks,CNN)相结合,提出了一种基于时空卷积神经网络(Spatiotemporal-CNN,ST-CNN)的交通标志实时检测识别算法。实验结果表明,该算法可对视频图像序列中的同一交通标志实现检测、筛选、追踪和识别处理,在保证高准确率的同时,可有效减少CNN的数据输入,降低系统资源占用量,提高计算效率,满足了视频中交通标志检测识别的实时性要求。算法平均每帧耗时26.82ms,且识别准确率达到96.94%。
中图分类号:
[1]LUETTEL T,HIMMELSBACH M,WUENSCHE H J.Autonomous Ground Vehicles—Concepts and a Path to the Future [J].Proceedings of the IEEE,2012,100(5):1831-1839. [2]赵娜,袁家斌,徐晗.智能交通系统综述[J].计算机科学,2014,41(11):7-11. [3]LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single Shot MultiBox Detector [M]∥Computer Vision-ECCV 2016.Chain:Springer,2016. [4]REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection [J].arXiv:1506.02640,2015. [5]GAO X W,PODLADCHIKOVA L N,SHAPOSHNIKOV D G,et al.Recognition of traffic signs based on their colour and shape features extracted using human vision models[J].Journal of Visual Communication & Image Representation,2006,17(4):675-685. [6]KUO W J,LIN C C.Two-Stage Road Sign Detection and Recognition[C]∥2007 IEEE International Conference on Multimedia and Expo.IEEE,2007. [7]JIANG Y,ZHOU S,JIANG Y,et al.Traffic sign recognitionusing Ridge Regression and OTSU method[C]∥Intelligent Vehicles Symposium.IEEE,2011. [8]UIJLINGS J R R,VAN DE SANDE K E A.Selective Search for Object Recognition [J].International Journal of Computer Vision,2013,104(2):154-171. [9]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,39(16):1137-1149. [10]HE K,GKIOXARI G,DOLLÁR P,et al.Mask R-CNN [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,PP(99):1-1. [11]STALLKAMP J,SCHLIPSING M,SALMEN J,et al.Man vs.computer:Benchmarking machine learning algorithms for traffic sign recognition [J].Neural newtorks:the official Journal of the International Neural Network Society,2012,32(2):323-332. [12]WU Y,LIU Y,LI J,et al.Traffic sign detection based on convolutional neural networks[C]∥International Joint Conference on Neural Networks.IEEE,2014. [13]GREENHALGH J,MIRMEHDI M.Real-Time Detection andRecognition of Road Traffic Signs [J].IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1498-1506. [14]IGEL C.Detection of traffic signs in real-world images:the German traffic sign detection benchmark[C]∥International Joint Conference on Neural Networks.IEEE,2013. [15]WANG G,REN G,WU Z,et al.A robust,coarse-to-fine traffic sign detection method[C]∥International Joint Conference on Neural Networks.IEEE,2013. [16]STALLKAMP J,SCHLIPSING M,et al.The German Traffic Sign Recognition Benchmark:A multi-class classification competition[C]∥International Joint Conference on Neural Networks.IEEE,2011. [17]LU X,WANG Y,ZHOU X,et al.Traffic Sign Recognition via Multi-Modal Tree-Structure Embedded Multi-Task Learning [J].IEEE Transactions on Intelligent Transportation Systems,2017,18(4):960-972. [18]LUO H,YANG Y,TONG B,et al.Traffic Sign RecognitionUsing a Multi-Task Convolutional Neural Network [J].IEEE Transactions on Intelligent Transportation Systems,2018,PP (99):1-12. [19]SERMANET P,LECUN Y.Traffic Sign Recognition withMulti-Scale Convolutional Networks[C]∥The 2011 International Joint Conference on Neural Networks (IJCNN).2011. |
[1] | 胡聪, 何晓晖, 邵发明, 张艳武, 卢冠林, 王金康. 基于极大极稳定区域及SVM的交通标志检测 Traffic Sign Detection Based on MSERs and SVM 计算机科学, 2022, 49(6A): 325-330. https://doi.org/10.11896/jsjkx.210300117 |
[2] | 吴培培, 吴兆贤, 唐文兵. 基于吸收态马尔可夫链的智能无人车系统实时性能分析 Real-time Performance Analysis of Intelligent Unmanned Vehicle System Based on Absorbing Markov Chain 计算机科学, 2021, 48(11A): 147-153. https://doi.org/10.11896/jsjkx.210300050 |
[3] | 张元鸣, 李梦妮, 黄浪游, 陆佳炜, 肖刚. 基于增量日志的数据组合视图定位更新方法 Data Composition View Positioning Update Approach with Incremental Logs 计算机科学, 2020, 47(6): 85-91. https://doi.org/10.11896/jsjkx.190500085 |
[4] | 庞宇, 刘平, 雷印杰. 基于移动端的“非受控”物体识别算法的实现 Realization of “Uncontrolled” Object Recognition Algorithm Based on Mobile Terminal 计算机科学, 2019, 46(6A): 153-157. |
[5] | 李宗鑫, 秦勃, 王梦倩. 基于时空关系模型的交通信号灯的实时检测与识别 Real-time Detection and Recognition of Traffic Light Based on Time-Space Model 计算机科学, 2018, 45(6): 314-319. https://doi.org/10.11896/j.issn.1002-137X.2018.06.055 |
[6] | 黄中平,白光伟,沈航,承骁,华志翔. 基于MapReduce模型的推测执行优化算法 Speculative Execution Optimization Algorithm with MapReduce 计算机科学, 2017, 44(4): 193-196. https://doi.org/10.11896/j.issn.1002-137X.2017.04.042 |
[7] | 孙磊,杨海燕,吴际. 基于IMA平台的嵌入式软件设计模型仿真及实时性分析方法 Simulation and Real-time Analysis for Embedded Software Design Model with Consideration of Integrated Modular Avionics Platform 计算机科学, 2015, 42(12): 95-97. |
[8] | 郑远力,胡志坤. 基于滑动扫描框的高速物体的图像实时跟踪算法 Real-time Tracking Algorithm for Fast Target Based on Dynamical Scanning Boxes 计算机科学, 2015, 42(10): 287-291. |
[9] | 王棚飞,刘宏哲,袁家政,陈丽. 一种基于图像匹配的地面交通标志实时识别方法 Method of Real Time Recognition of Ground Traffic Signs Based on Image Matching 计算机科学, 2014, 41(6): 317-325. https://doi.org/10.11896/j.issn.1002-137X.2014.06.064 |
[10] | 柏骏,夏靖波,吴吉祥,任高明,赵小欢. 实时网络流量分类研究综述 Survey on Real-time Traffic Classification 计算机科学, 2013, 40(9): 8-15. |
[11] | 余云霞,綦志勇. 基于混沌反馈控制理论的资源选择算法研究 Research on Resource Selection Algorithm Based on Feedback Control of Chaos Theory 计算机科学, 2012, 39(Z6): 452-456. |
[12] | 李 允,桂盛霖,陈 更,罗 蕾. 嵌入式实时软件模型开发环境研究 Model Development Environment Research of Embedded Real-time Software 计算机科学, 2012, 39(Z11): 226-229. |
[13] | 唐国明,周广新,谢羿,汤大权,唐九阳. 一种基于双层栅格划分的无线传感器网络目标定位方法 Target Localization Based on Double-level Grid Division in Wireless Sensor Networks 计算机科学, 2012, 39(6): 25-29. |
[14] | 周伟,安虹,刘谷,李小强,吴石磊. 一种输入感知的雷达回波快速聚类实现 Input-aware Runtime Scheduling Support for Fast Clustering of Radar Reflectivity Data on GPUs 计算机科学, 2012, 39(12): 295-299. |
[15] | 范荣全,肖红,李琪林. 智能变电站GOOSE通信网实时性分析 Real-time Performance Analysis of GOOSE Communication Network in Smart Substation 计算机科学, 2011, 38(Z10): 444-446. |
|