Road Detection Based on Statistical Analysis
Abstract
Vision-based road detection is a challenging topic in the field of autonomous vehicles. Road information is needed for a robot vehicle to control their movement while driving. The aim of this study is to make a simple and fast road detection system which can be used to control vehicle robots in real-time. This paper proposes real-time road detection, which begins from images captured on a camera, then pre-processing and road detection based on color and statistical analysis, for reducing the detection failure rate. The dataset used for the system was taken from urban roads. The color-matching procedure uses minimum/maximum matching between the dataset and sample pixels of captured images. At the same time, the captured images are clustered by a grid method, grouping pixels having the same size between grid cluster and dataset. PSNR and MSSIM are used for analyzing the similarity of the grid cluster and dataset. We implemented a threshold value to determine if the PSNR and MSSIM analysis result is a road. From our experiment, we obtain a threshold value of 85 db for PSNR and 0.90 for MSSIM. If the both analyses result in values greater than their threshold values, then the result is voted to be a road. Finally, the road is determined by analyzing the result of color and statistical analysis. The system will determine there is a road if the both analyses detect the road. The result shows the color based road detection resulting file detection caused by changing the lighting and the color of the road. We have solved that problem by implementing PSNR and MSSIM analysis in the grid clustering method. The result of the proposed method has been presented in this paper, and can potentially reduce the failure rate of detection using the color-based method.
Keywords: road labeling, image measurement, mean structural similarity index measurement.
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SOMAWIRATA I. K., & UTAMININGRUM F. Road Detection Based on the Color Space and Cluster Connecting. Proceedings of the International Conference on Signal and Image Processing, Beijing, 2016, pp. 118-122. https://doi.org/10.1109/SIPROCESS.2016.7888235
FRITSCH J., KUHNL T., and GEIGER A. A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms. Proceedings of the International IEEE Conference on Intelligent Transportation Systems, The Hague, 2013, pp. 1693-1700. https://doi.org/10.1109/ITSC.2013.6728473
KONG H., AUDIBERT J.-Y., and PONCE J. Vanishing point detection for road detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, 2009, pp. 96-103. https://doi.org/10.1109/CVPR.2009.5206787
DING W., & LI Y. Efficient Vanishing Point Detection Method in Complex Urban Road Environments. IET Computer Vision, 2015, 9(4): 549-558. https://doi.org/10.1049/iet-cvi.2014.0187
WEILI D., YONG L., WENFENG W., and YUANYUAN Z. Vanishing point detection algorithm for urban road image based on the envelope of perpendicular and parallel lines. Acta Optica Sinica, 2014, 34(10): 1015002. https://doi.org/10.3788/AOS201434.1015002
KONG H., AUDIBERT J.-Y., and PONCE J. General road detection from a single image. IEEE Transactions on Image Processing, 2010, 19(8): 2211–2220. https://doi.org/10.1109/TIP.2010.2045715
ZHENG T., CHENG X., CHAO M., et al. Road Segmentation Based on Vanishing Point and Principal Orientation Estimation. Journal of Computer Research and Development, 2014, 51(4): 762-772.
XIAOLIN L., YUFENG J., YAN G., XIAOXUE F., and WEIXING L. Unstructured road detection based on region growing. Proceedings of the IEEE 30th Chinese Control and Decision Conference, Shenyang, 2018, pp. 3451-3456. https://doi.org/10.1109/CCDC.2018.8407720
YAO J., RAMALINGAM S., TAGUCHI Y., MIKI Y., and URTASUN R. Estimating drivable collision-free space from monocular video. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, 2015, pp. 420-427. https://doi.org/10.1109/WACV.2015.62
LEVI D., GARNETT N., FETAYA E., and HERZLYIA I. Stixelnet: A deep convolutional network for obstacle detection and road segmentation. Proceedings of the British Machine Vision Conference, Swansea, 2015, pp. 109.1-109.12. https://dx.doi.org/10.5244/C.29.109
MOHAN R. Deep deconvolutional networks for scene parsing, 2014. https://arxiv.org/pdf/1411.4101.pdf
CHENG H.-Y., JENG B.-S., TSENG P.-T., and FAN K.-C. Lane detection with moving vehicles in the traffic scenes. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(4): 571-582. https://doi.org/10.1109/TITS.2006.883940
AL-ABAID S. A. F. A Smart Traffic Control System Using Image Processing: A Review. Journal of Southwest Jiaotong University, 2020, 55(1). https://doi.org/10.35741/issn.0258-2724.55.1.31
PAZ L. M., PINIÉS P., and NEWMAN P. A Variational Approach to Online Road and Path Segmentation with Monocular Vision. Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, Washington, 2015, pp. 1633-1639. https://doi.org/10.1109/ICRA.2015.7139407
LIEB D., LOOKINGBILL A., and THRUN S. Adaptive road following using self-supervised learning and reverse optical flow. Robotics: Science and Systems, 2005, 1: 273–280. https://doi.org/10.15607/rss.2005.i.036
TAN C., HONG T., CHANG T., and SHNEIER M. Color model-based real-time learning for road following. Proceedings of the IEEE Intelligent Transportation Systems Conference, Toronto, 2006, pp. 939-944. https://doi.org/10.1109/ITSC.2006.1706865
WANG Z., BOVIK A. C., SHEIKH H. R., and SIMONCELL E. P. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transaction on Image Processing, 2004, 13(4): 600-612. https://doi.org/10.1109/TIP.2003.819861
YANHUI W., CHUAN Y., and YUHONG D. Multi-scale Extraction of Road Network Incremental Information in Navigation Electronic Map. Journal of Southwest Jiaotong University, 2015, 50(4). http://jsju.org/index.php/journal/article/view/154
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