[1] Zhang Ning, He Tiejun, Gao Zhaohui, Huang Wei, et al. Traffic light detection and recognition in intersectionsbased on intelligent vehicle [J]. Journal of Southeast University (English Edition), 2008, 24 (4): 517-521. [doi:10.3969/j.issn.1003-7985.2008.04.024]
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Traffic light detection and recognition in intersectionsbased on intelligent vehicle(
)
智能车辆的交叉口数字信号灯检测与识别
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 24
- Issue:
- 2008 4
- Page:
- 517-521
- Research Field:
- Traffic and Transportation Engineering
- Publishing date:
- 2008-12-30
Info
- Title:
- Traffic light detection and recognition in intersectionsbased on intelligent vehicle
- 智能车辆的交叉口数字信号灯检测与识别
- Author(s):
- Zhang Ning; He Tiejun; Gao Zhaohui; Huang Wei
- ITS Research Center, Southeast University, Nanjing 210096, China
- Keywords:
- intelligent vehicle; stabling siding detection; traffic lights detection; self-associative memory; light-emitting diode(LED)characters recognition
- PACS:
- U491
- DOI:
- 10.3969/j.issn.1003-7985.2008.04.024
- Abstract:
- To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights.First, the stabling siding at intersections is detected by applying Hough transformation.Then, the colors of traffic lights are detected with color space transformation.Finally, self-associative memory is used to recognize the countdown characters of the traffic lights.Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively.The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.
- 为了保证智能车辆在交叉口内的诱导行驶, 提出了一种交叉口信号灯检测与识别方法.首先利用Hough变换检测交叉口内的停车线, 然后采用颜色空间变换检测红、黄、绿三色信号灯, 最后建立自联想存储器以识别切分出来的信号灯时间字符.通过20个实际交叉口场景测试数据验证, 采用所提出的方法, 停车线检测正确率达90%, 信号灯检测正确率为85%, 在信号灯字符正确分割出来的基础上, 字符的识别率达97%.结果表明提出的方法能够十分有效地进行数字信号灯的检测, 并具有足够的鲁棒性识别“破损”及带噪声的字符.
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Memo
- Memo:
-
Biography: Zhang Ning(1972—), male, doctor, associate professor, ningzhang1972@yahoo.com.cn.
Foundation item: The Cultivation Fund of the Key Scientific and Technical Innovation Project of Higher Education of Ministry of Education(No.705020).
Citation: Zhang Ning, He Tiejun, Gao Zhaohui, et al.Traffic light detection and recognition in intersections based on intelligent vehicle[J].Journal of Southeast University(English Edition), 2008, 24(4):517-521.