Research Article |
A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces
Author(s): Pankaj Prusty* and Bibhu Prasad Mohanty
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 11, Issue 3
Publisher : FOREX Publication
Published : 23 September 2023
e-ISSN : 2347-470X
Page(s) : 773-780
Abstract
In advanced driver assistance system detection of road surfaces is an important task. Few algorithms have been proposed in past to detect the road surfaces based on intensities. However, problem arises in detection process is due to the presence of shadows or wet road surfaces. Here we have proposed a novel algorithm for detection of shadows with the help of machine learning approaches. Initially shadow is being detected with the help of a threshold-based approach followed by windowing-based method. The detected shadow region gets confirmed with the help of a set of features and classifier. The detected shadow or wet pixels are in painted to obtain set of pixels without shadow for road classification problems. The simplicity and accuracy of the algorithm makes it robust and can be used as a part of road surface detection algorithm.
Keywords: Intelligent vehicle
, ADAS
, Forward difference
, Seed point
, Windowing
.
Pankaj Prusty*, Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan Deemed to be University Khandagiri, Bhubaneswar, Odisha, India; Email: pankajprusty@soa.ac.in
Bibhu Prasad Mohanty, Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan Deemed to be University Khandagiri, Bhubaneswar, Odisha, India;
-
[1] Yang, G., Li, Q. J., Zhan, Y. J., Wang, K. C., & Wang, C. (2018). Wavelet based macrotexture analysis for pavement friction prediction. KSCE Journal of Civil Engineering, 22, 117-124.
-
[2] Huyan, J., Li, W., Tighe, S., Sun, Z., & Sun, H. (2020). Quantitative analysis of macrotexture of asphalt concrete pavement surface based on 3D data. Transportation Research Record, 2674(8), 732-744.
-
[3] Tong, Z., Gao, J., Sha, A., Hu, L., & Li, S. (2018). Convolutional neural network for asphalt pavement surface texture analysis. Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1056-1072.
-
[4] Raj, A., Krishna, D., Priya, R. H., Shantanu, K., & Devi, S. N. (2012, September). Vision based road surface detection for automotive systems. In 2012 International Conference on Applied Electronics (pp. 223-228). IEEE.
-
[5] Gawande, U., Hajari, K., & Golhar, Y. (2020). Pedestrian detection and tracking in video surveillance system: issues, comprehensive review, and challenges. Recent Trends in Computational Intelligence, 1-24.
-
[6] Aleksi, I., Matić, T., Lehmann, B., & Kraus, D. (2020). Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images. International journal of electrical and computer engineering systems, 11(2), 53-66.
-
[7] Noaman, R. A., Ali, M. A. M., Zainal, N., & Saeed, F. (2016). Human Detection Framework for Automated Surveillance Systems. International Journal of Electrical & Computer Engineering (2088-8708), 6(2).
-
[8] Jyothisree, V., & Dharan, S. (2013). Shadow detection using tricolor attenuation model enhanced with adaptive histogram equalization. International Journal of Computer Science & Information Technology, 5(2), 147-155.
-
[9] Amin, B., Riaz, M. M., & Ghafoor, A. (2020). Automatic shadow detection and removal using image matting. Signal Processing, 170, 107415.
-
[10] M. I. Thariq Hussan, D. Saidulu, P. T. Anitha, A. Manikandan and P. Naresh (2022), Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People. IJEER 10(2), 80-86. DOI: 10.37391/IJEER.100205.
-
[11] Madhuri A. Tayal, Minal Deshmukh, Vijaya Pangave, Manjushri Joshi, Sulakshana Malwade and Shraddha Ovale (2023), VMLHST: Development of an Efficient Novel Virtual Reality ML Framework with Haptic Feedbacks for Improving Sports Training Scenarios. ijeer 11(2), 601-608. DOI: 10.37391/ijeer.110249.
-
[12] Singh, K. K., Pal, K., & Nigam, M. J. (2012). Shadow detection and removal from remote sensing images using NDI and morphological operators. International journal of computer applications, 42(10), 37-40.
-
[13] Tian, J., Sun, J., & Tang, Y. (2009). Tricolor attenuation model for shadow detection. IEEE Transactions on image processing, 18(10), 2355-2363.
-
[14] Weian, S. (2021, April). A road extraction algorithm for reducing shadow effect. In 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 752-756). IEEE.
-
[15] Li, B., & Cheng, Y. (2021, January). Shadow Detection in Slope Monitoring Based on Digital Image Processing. In 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) (pp. 320-323). IEEE.
-
[16] Khan, M. W., Dunning, S., Bainbridge, R., Martin, J., Diaz-Moreno, A., Torun, H., ... & Lim, M. (2021). Low-cost automatic slope monitoring using vector tracking analyses on live-streamed time-lapse imagery. Remote Sensing, 13(5), 893.
-
[17] Panicker, J. V., & Wilscy, M. (2010, February). Detection of moving cast shadows using edge information. In 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) (Vol. 5,pp. 817-821). IEEE.
-
[18] Hou, Y., Li, Q., Zhang, C., Lu, G., Ye, Z., Chen, Y., ... & Cao, D. (2021). The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis. Engineering, 7(6), 845-856.
-
[19] Benmhahe, B., & Chentoufi, J. A. (2021). Automated pavement distress detection, classification, and measurement: a review. International Journal of Advanced Computer Science and Applications, 12(8).
-
[20] Mei, Q., & Gül, M. (2020). Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones. Structural Health Monitoring, 19(6), 1726-1744.
-
[21] Mitchel, T. (1997). Machine Learning, McGraw-Hill Education (ISE Editions).
-
[22] Murti, Risky Perdana Adiperkasa, Sandika Maulana Putra, Sulton Aji Kurniawan, and Youngga Rega Nugraha. "Naïve Bayes Classifier for Journal Quartile Classification."
-
[23] Rish, I. (2001, August). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence (Vol. 3, No. 22, pp. 41-46).
-
[24] Sahoo, T., & Mohanty, B. (2022). A Novel Region Growing Algorithm using Wavelet Coefficient Feature Combination of Image Dynamics. International Journal of Advanced Computer Science and Applications, 13(6).
-
[25] Shilpa, M., Gopalakrishna, M. T., & Naveena, C. (2020). Approach for shadow detection and removal using machine learning techniques. IET Image Processing, 14(13), 2998-3005.