[1]WU Xiru,QIU Taotao.Improved Faster R-CNN vehicle instrument pointer real-time detection algorithm[J].CAAI Transactions on Intelligent Systems,2021,16(6):1056-1063.[doi:10.11992/tis.202011003]
Copy

Improved Faster R-CNN vehicle instrument pointer real-time detection algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1056-1063 Column: 学术论文—机器感知与模式识别 Public date: 2021-11-05
Title:
Improved Faster R-CNN vehicle instrument pointer real-time detection algorithm
Author(s):
WU Xiru;  QIU Taotao
College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
Keywords:
convolutional neural network; vehicle instrument pointer; real-time detection; bilinear interpolation; deep learning; pattern recognition; feature extraction; feature aggregation
CLC:
TP183;TP391.41
DOI:
10.11992/tis.202011003
Abstract:
This paper proposes an improved Faster R-CNN vehicle instrument pointer real-time detection algorithm to solve the problems of the poor artificial visual detection effect, slow detection speed, and low real-time performance of industrialized vehicle instrument pointers. First, complete transfer between the high- and low-layer features of a small target is realized by improving an original RoI network layer structure. Subsequently, continuous feature aggregation reduces calculation time using a bilinear interpolation algorithm to replace two quantization operations. Finally, video data collected by an industrial machine are preprocessed into a VOC format data set for training, and hyperparameters are adjusted to obtain an improved vehicle instrument pointer detection model. Experimental results show that the proposed method can quickly and accurately detect the vehicle instrument pointer. The average detection time of a single picture is 0.197 s, and the average detection accuracy can reach 92.7%. The good generalization performance of this method is demonstrated in the migration experiment of different instrument pointer types.
References:
[1] BEHAINE C A R, SCHARCANSKI J. Remote visual monitoring of analogue meter displays using deformable models[J]. IET science, measurement & technology, 2014, 8(4): 228-235.
[2] BAO Haojing, TAN Qingchang, LIU Siyuan, et al. Computer vision measurement of pointer meter readings based on inverse perspective mapping[J]. Applied sciences, 2019, 9(18): 3729.
[3] 韩绍超, 徐遵义, 尹中川, 等. 指针式仪表自动读数识别技术的研究现状与发展[J]. 计算机科学, 2018, 45(S1): 54-57
HAN Shaochao, XU Zunyi, YIN Zhongchuan, et al. Research review and development for automatic reading recognition technology of pointer instruments[J]. Computer science, 2018, 45(S1): 54-57
[4] 袁立祥, 龙斌, 杨志伟, 等. 基于智能网络信息系统的车轮加工自动化生产线设计与应用[J]. 机械设计, 2019, 36(S1): 295-297
YUAN Lixiang, LONG Bin, YANG Zhiwei, et al. Design and application of wheel automatically processing production line based on intelligent network information system[J]. Journal of machine design, 2019, 36(S1): 295-297
[5] 李光平, 唐月夏. 自动化生产线装配单元三维仿真平台的构建[J]. 实验技术与管理, 2020, 37(1): 141-144, 148
LI Guangping, TANG Yuexia. Construction of 3D simulation platform for assembly unit of automatic production line[J]. Experimental technology and management, 2020, 37(1): 141-144, 148
[6] 杨虹, 张雅声, 尹灿斌. 空间目标的ISAR成像及轮廓特征提取[J]. 北京航空航天大学学报, 2019, 45(9): 1765-1776
YANG Hong, ZHANG Yasheng, YIN Canbin. ISAR imaging and contour feature extraction of space targets[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1765-1776
[7] ALEGRIA E C, SERRA A C. Automatic calibration of analog and digital measuring instruments using computer vision[J]. IEEE transactions on instrumentation and measurement, 2000, 49(1): 94-99.
[8] 岳娟, 高思莉, 李范鸣, 等. 具有近似仿射尺度不变特征的快速图像匹配[J]. 光学精密工程, 2020, 28(10): 2349-2359
YUE Juan, GAO Sili, LI Fanming, et al. Fast image matching algorithm with approximate affine and scale invariance[J]. Optics and precision engineering, 2020, 28(10): 2349-2359
[9] JAFFERY Z A, DUBEY A K. Architecture of noninvasive real time visual monitoring system for dial type measuring instrument[J]. IEEE sensors journal, 2013, 13(4): 1236-1244.
[10] CHI Jiannan, LIU Lei, LIU Jiwei, et al. Machine vision based automatic detection method of indicating values of a pointer gauge[J]. Mathematical problems in engineering, 2015, 2015: 1-19.
[11] HAO Zelong, CHEN Xuhui, HU Jianqiang, et al. OpenCV-based automatic detection system for automobile meter[J]. Applied mechanics and materials, 2014, 615: 149-152.
[12] GAO Huijun, YI Ming, YU Jinyong, et al. Character segmentation-based coarse-fine approach for automobile dashboard detection[J]. IEEE transactions on industrial informatics, 2019, 15(10): 5413-5424.
[13] 王建新, 王子亚, 田萱. 基于深度学习的自然场景文本检测与识别综述[J]. 软件学报, 2020, 31(5): 1465-1496
WANG Jianxin, WANG Ziya, TIAN Xuan. Review of natural scene text detection and recognition based on deep learning[J]. Journal of software, 2020, 31(5): 1465-1496
[14] HOU Tianyue, AN Yi, CHANG Qi, et al. Deep-learning-assisted, two-stage phase control method for high-power mode-programmable orbital angular momentum beam generation[J]. Photonics research, 2020, 8(5): 715-722.
[15] 张新钰, 高洪波, 赵建辉, 等. 基于深度学习的自动驾驶技术综述[J]. 清华大学学报(自然科学版), 2018, 58(4): 438-444
ZHANG Xinyu, GAO Hongbo, ZHAO Jianhui, et al. Overview of deep learning intelligent driving methods[J]. Journal of Tsinghua University (science and technology edition), 2018, 58(4): 438-444
[16] 曹锦纲, 李金华, 郑顾平. 基于生成式对抗网络的道路交通模糊图像增强[J]. 智能系统学报, 2020, 15(3): 491-498
CAO Jingang, LI Jinhua, ZHENG Guping. Enhancement of blurred road-traffic images based on generative adversarial network[J]. CAAI transactions on intelligent systems, 2020, 15(3): 491-498
[17] 曲仕茹, 席玉玲, 丁松涛. 基于深度学习的交通场景语义描述[J]. 西北工业大学学报, 2018, 36(3): 522-527
QU Shiru, XI Yuling, DING Songtao. Image caption description of traffic scene based on deep learning[J]. Journal of Northwestern Polytechnical University, 2018, 36(3): 522-527
[18] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[19] WU Wenqi, YIN Yingjie, WANG Xingang, et al. Face detection with different scales based on Faster R-CNN[J]. IEEE transactions on cybernetics, 2019, 49(11): 4017-4028.
[20] 徐岩, 陶慧青, 虎丽丽. 基于Faster R-CNN网络模型的铁路异物侵限检测算法研究[J]. 铁道学报, 2020, 42(5): 91-98
XU Yan, TAO Huiqing, HU Lili. Railway foreign body intrusion detection based on Faster R-CNN network model[J]. Journal of the China railway society, 2020, 42(5): 91-98
[21] FANG Fen, LI Liyuan, ZHU Hongyuan, et al. Combining Faster R-CNN and model-driven clustering for elongated object detection[J]. IEEE transactions on image processing, 2019, 29: 2052-2065.
[22] LIU Xiaobo, WANG Huaiyuan, JING Hongdi, et al. Research on intelligent identification of rock types based on Faster R-CNN method[J]. IEEE access, 2020, 8: 21804-21812.
[23] CHENG Gong, HAN Junwei, ZHOU Peicheng, et al. Learning rotation-invariant and Fisher discriminative convolutional neural networks for object detection[J]. IEEE transactions on image processing, 2019, 28(1): 265-278.
[24] 刘召, 张黎明, 耿美晓, 等. 基于改进的Faster R-CNN高压线缆目标检测方法[J]. 智能系统学报, 2019, 14(4): 627-634
LIU Zhao, ZHANG Liming, GENG Meixiao, et al. Object detection of high-voltage cable based on improved Faster R-CNN[J]. CAAI transactions on intelligent systems, 2019, 14(4): 627-634
[25] EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The PASCAL visual object classes (VOC) challenge[J]. International journal of computer vision, 2010, 88(2): 303-338.
Similar References:

Memo

-

Last Update: 2021-12-25

Copyright © CAAI Transactions on Intelligent Systems