Distance Posts Detection and Character Sequences Recognition Method in Video Images Acquired from Camera in Moving Vehicle

doi:10.3778/j.issn.1002-8331.2112-0246

Abstract

Abstract: The problem of vehicle location in GPS denied environments can be solved with the video images positioning method, which detects the highway distance posts and recognizes characters to obtain their geographic information through information retrieval. In this paper, resulting from few types of distance posts, it puts forward S-YOLOv3（simplified YOLOv3） to calculate more fastly. In order to improve the accuracy of character recognition, it proposes HPCC-YOLOv3 （high precision character classification YOLOv3）. S-YOLOv3 and HPCC-YOLOv3 are trained and tested respectively. Characters are clustered according to position in kilometer posts and hectometer posts to realize character recognition. An images acquisition, highway distance posts detection and character recognition system composed of the Daheng mercury camera and a computer is designed. With images acquired from Daheng mercury camera in the experimental car, it gets the results showing that the system performs well, which can effectively improve the speed of highway distance posts detection and the accuracy of character recognition in video images.

摘要： 视频图像定位方法可以解决GPS拒止环境下行车定位问题，该方法对公路路牌检测与字符序列识别，通过地理信息检索，得到路牌所在位置的地理信息。针对路牌检测类别少的问题，对YOLOv3进行轻量化改造，提出了简化的YOLOv3（simplified YOLOv3，S-YOLOv3）；为了提高字符分类精度，对YOLOv3进行特征融合策略改进，提出高精度的字符分类YOLOv3（high precision character classification YOLOv3，HPCC-YOLOv3）；分别对S-YOLOv3与HPCC-YOLOv3进行训练与测试；按照字符检测结果所处的位置进行字符聚类，实现字符序列识别；设计了由车载大恒水星相机、计算机组成的图像采集、车牌检测与字符识别系统；在复杂环境下进行跑车实验，结果表明了提出的方法能够有效提高视频图像路牌目标检测的速度和字符序列识别的精度。

LIU Xiaoxi, CHENG Jiacheng, CHENG Yongmei, GU Yifan, LEI Xinhua, WANG Bo. Distance Posts Detection and Character Sequences Recognition Method in Video Images Acquired from Camera in Moving Vehicle[J]. Computer Engineering and Applications, 2023, 59(8): 175-181.

刘小溪, 程佳诚, 程咏梅, 顾一凡, 雷鑫华, 汪波. 车载视频图像路牌检测与字符序列识别方法[J]. 计算机工程与应用, 2023, 59(8): 175-181.

References

[1] QIN T，LI P，SHEN S.Vins-mono：a robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics，2018，34（4）：1004-1020.
[2] KLEIN G，MURRAY D.Parallel tracking and mapping for small AR workspaces[C]//6th IEEE and ACM International Symposium on Mixed and Augmented Reality，2007：225-234.
[3] WONG D，DEGUCHI D，IDE I，et al.Single camera vehicle localization using SURF scale and dynamic time warping[C]//IEEE Intelligent Vehicles Symposium Proceedings，2014：681-686.
[4] PINK O，MOOSMANN F，BACHMANN A.Visual features for vehicle localization and ego-motion estimation[C]//IEEE Intelligent Vehicles Symposium，2009：254-260.
[5] COHEN A C J，KOSS F V.Comprehensive study of three-object triangulation[J].Proceedings of SPIE-The International Society for Optical Engineering，1998：95-106.
[6] 冯琦.基于自定义视觉路标的车辆自定位方法研究[D].沈阳：东北大学，2012.
FENG Qi.Research of vehicles self-localization method based on custom vision landmarks[D].Shenyang：Northeastern University，2012.
[7] QIAN Rongqiang，ZHANG Bailing，YUE Yong，et al.Robust Chinese traffic sign detection and recognition with deep convolutional neural network[C]//2015 11th International Conference on Natural Computation（ICNC），2015：791-796.
[8] YUAN T L，ZHU Z，XU K，et al.A large Chinese text dataset in the wild[J].Journal of Computer Science and Technology，2019，34（3）：509-521.
[9] GIRSHICK R，DONAHUE J，DARRELL T，et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2014.
[10] HE K，ZHANG X，REN S，et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence，2014，37（9）：1904-1916.
[11] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision（ICCV），2015：1440-1448.
[12] 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，2017，39（6）：1137-1149.
[13] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//14th European Conference on Computer Vision（ECCV），2016：21-37.
[14] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：unified，real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：779-788.
[15] REDMON J，FARHADI A.YOLOv3：an incremental improvement[C]//IEEE Conference on Computer Vision and Pattern Recognition，2018.
[16] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//IEEE Conference on Computer Visionand Pattern Recognition，2017：7263-7271.
[17] SHI B，XIANG B，CONG Y.An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence，2016，39（11）：2298-2304.
[18] SHI B，WANG X，LV P，et al.Robust scene text recognition with automatic rectification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2016：4168-4176.
[19] LIAO Minghui，ZHANG Jian，WAN Zhaoyi，et al.Scene text recognition from two-dimensional perspective[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：8714-8721.
[20] LIU X，DING L，SHI Y，et al.FOTS：fast oriented text spotting with a unified network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR），2018.
[21] LYU P，LIAO M，YAO C，et al.Mask TextSpotter：an end-to-end trainable neural network for spotting text with arbitrary shapes[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：67-83.
[22] LIU Y，CHEN H，SHEN C，et al.ABCNet：real-time scene text spotting with adaptive Bezier-curve network[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR），2020.
[23] ZHENG Z，WANG P，REN D，et al.Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J].IEEE Transactions on Cybernetics，2022，52（8）：8574-8586.
[24] EVERINGHAM M，WINN J.The PASCAL visual object classes challenge 2007（VOC2007） development kit[J].International Journal of Computer Vision，2006，111（1）：98-136.
[25] HRIPCSAK G，ROTHSCHILD A S.Technical brief：agreement，the F-Measure，and reliability in information retrieval[J].Journal of the American Medical Informatics Association，2005，12（3）：296-298.