In this paper, approaches are presented for the thresholding, detection, tracking and recognition of the road signs as part of an Advanced Driver Assistance System (ADAS). In all these approaches, feature extraction is the backbone, whereas detection and recognition require the use of detectors and classifiers, respectively. In this, two issues are dominant: 1) Tackling the variability involved in the lighting conditions, sizes, and shapes of the road signs after segregating them from a world scene, and 2) Focusing on inaccurate fuzzy modeling arising due to the improper distribution of pixel intensities. The variability is overcome with the uncertainty representation using the information sets, an extension of fuzzy sets, whereas the incorrect fuzzy modeling is rectified using the pervasive information sets, an extension of intuitionistic fuzzy sets. The development of the intuitionistic fuzzy transform paralleling the fuzzy entropy function paves the way for the formulation of different hesitancy features by cashing in on the non-membership function. Next, promulgation of the Hanman law prescribes the fuzzy gradient/divergent values for different tasks. The notable landmarks of this work are the creation of a Color-Based Detector (CBD), derivation of the incremental hesitancy features accrued from the color histograms and the formulation of a variant of the Hanman Transform Classifier using Convolutional Neural Network (CNN) features. We have used the Belgium dataset to vindicate the efficacy of the proposed methods.
References
[1]
Mathias, M., Timofte, R., Benenson, R. and Van Gool, L. (2013) Traffic Sign Recognition—How Far Are We from the Solution? The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, 4-9 August 2013, 1-8. https://doi.org/10.1109/ijcnn.2013.6707049
[2]
Mogelmose, A., Liu, D. and Trivedi, M.M. (2015) Detection of U.S. Traffic Signs. IEEETransactionsonIntelligentTransportationSystems, 16, 3116-3125. https://doi.org/10.1109/tits.2015.2433019
[3]
Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B. and Hu, S. (2016) Traffic-Sign Detection and Classification in the Wild. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 2110-2118. https://doi.org/10.1109/cvpr.2016.232
[4]
Arcos-García, Á., Álvarez-García, J.A. and Soria-Morillo, L.M. (2018) Evaluation of Deep Neural Networks for Traffic Sign Detection Systems. Neurocomputing, 316, 332-344. https://doi.org/10.1016/j.neucom.2018.08.009
[5]
Güney, E. and Bayılmış, C. (2022) An Implementation of Traffic Signs and Road Objects Detection Using Faster R-CNN. Sakarya University Journal of Computer and InformationSciences, 5, 216-224. https://doi.org/10.35377/saucis...1073355
[6]
Hasegawa, R., Iwamoto, Y. and Chen, Y. (2020) Robust Japanese Road Sign Detection and Recognition in Complex Scenes Using Convolutional Neural Networks. JournalofImageandGraphics, 8, 59-66. https://doi.org/10.18178/joig.8.3.59-66
[7]
Zhang, J.M., Zou, X., Kuang, L.D., Wang, J., Simon Sherratt, R. and Yu, X. (2022) CCTSDB 2021: A More Comprehensive Traffic Sign Detection Benchmark. Human-centric Computing and Information Sciences, 12, Article No. 23. https://centaur.reading.ac.uk/106129/
[8]
Wang, J., Chen, Y., Dong, Z. and Gao, M. (2022) Improved Yolov5 Network for Real-Time Multi-Scale Traffic Sign Detection. Neural Computing and Applications, 35, 7853-7865. https://doi.org/10.1007/s00521-022-08077-5
[9]
Han, C., Gao, G. and Zhang, Y. (2018) Real-Time Small Traffic Sign Detection with Revised Faster-RCNN. MultimediaToolsandApplications, 78, 13263-13278. https://doi.org/10.1007/s11042-018-6428-0
[10]
Kamal, U., Tonmoy, T.I., Das, S. and Hasan, M.K. (2020) Automatic Traffic Sign Detection and Recognition Using Segu-Net and a Modified Tversky Loss Function with L1-Constraint. IEEE Transactions on Intelligent Transportation Systems, 21, 1467-1479. https://doi.org/10.1109/tits.2019.2911727
[11]
Liu, C., Li, S., Chang, F. and Wang, Y. (2019) Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives. IEEEAccess, 7, 86578-86596. https://doi.org/10.1109/access.2019.2924947
[12]
Lim, X.R., Lee, C.P., Lim, K.M., Ong, T.S., Alqahtani, A. and Ali, M. (2023) Recent Advances in Traffic Sign Recognition: Approaches and Datasets. Sensors, 23, Article 4674. https://doi.org/10.3390/s23104674
[13]
Zhu, Y. and Yan, W.Q. (2022) Traffic Sign Recognition Based on Deep Learning. MultimediaToolsandApplications, 81, 17779-17791. https://doi.org/10.1007/s11042-022-12163-0
[14]
Bayoudh, K., Hamdaoui, F. and Mtibaa, A. (2020) Transfer Learning Based Hybrid 2D-3D CNN for Traffic Sign Recognition and Semantic Road Detection Applied in Advanced Driver Assistance Systems. AppliedIntelligence, 51, 124-142. https://doi.org/10.1007/s10489-020-01801-5
[15]
Ojo, S., Olugbade, S. and Ojo, T.P. (2022) A Review of Road Accidents Detection through Wireless Technology—5g, MIMO and Internet of Things. OpenJournalofAppliedSciences, 12, 1968-1978. https://doi.org/10.4236/ojapps.2022.1212137
[16]
Milu, S.A., Fathima, A., Talukder, T., Islam, I. and Emon, M.I.S. (2024) Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development. Journal of Data Analysis and Information Processing, 12, 139-150. https://doi.org/10.4236/jdaip.2024.122008
[17]
Luo, Y., Ci, Y., Jiang, S. and Wei, X. (2024) A Novel Lightweight Real-Time Traffic Sign Detection Method Based on an Embedded Device and YOLOv8. JournalofReal-TimeImageProcessing, 21, Article No. 24. https://doi.org/10.1007/s11554-023-01403-7
[18]
Hu, T., Gong, Z.W. and Song, J. (2024) Research and Implementation of an Embedded Traffic Sign Detection Model Using Improved YOLOv5. International Journal of Automotive Technology, 25, 881-892.
[19]
Hanmandlu, M. (2011) Information Sets and Information Processing. DefenceScienceJournal, 61, 405-407. https://doi.org/10.14429/dsj.61.1192
[20]
Bansal, M. and Madasu, H. (2021) Ear-Based Authentication Using Information Sets and Information Modelling. SoftComputing, 25, 11123-11138. https://doi.org/10.1007/s00500-021-05858-3
[21]
Hanmandlu, M., Bansal, M. and Vasikarla, S. (2020) An Introduction to Information Sets with an Application to Iris Based Authentication. JournalofModernPhysics, 11, 122-144. https://doi.org/10.4236/jmp.2020.111008
Grover, J. and Hanmandlu, M. (2020) Novel Competitive-Cooperative Learning Models (CCLMs) Based on Higher Order Information Sets. Applied Intelligence, 51, 1513-1530. https://doi.org/10.1007/s10489-020-01881-3
[24]
Mamta, and Hanmandlu, M. (2014) A New Entropy Function and a Classifier for Thermal Face Recognition. Engineering Applications of Artificial Intelligence, 36, 269-286. https://doi.org/10.1016/j.engappai.2014.06.028
[25]
Asthana, P., Hanmandlu, M. and Vashisth, S. (2021) Classification of Brain Tumor from Magnetic Resonance Images Using Probabilistic Features and Possibilistic Hanman-Shannon Transform Classifier. International Journal of Imaging Systems and Technology, 32, 280-294. https://doi.org/10.1002/ima.22619
[26]
Grover, J. and Hanmandlu, M. (2022) Development of an Optimal Entropy Classifier and Prudent Learning Model. IEEE Transactions on Artificial Intelligence, 3, 164-175. https://doi.org/10.1109/tai.2021.3117491
[27]
Sermanet, P. and LeCun, Y. (2011) Traffic Sign Recognition with Multi-Scale Convolutional Networks. The 2011 International Joint Conference on Neural Networks, San Jose, 31 July-5 August 2011, 2809-2813. https://doi.org/10.1109/ijcnn.2011.6033589
[28]
Dabass, J., Hanmandlu, M., Vig, R. and Vasikarla, S. (2024) Mammogram Classification with Hanmannets Using Hanman Transform Classifier. Journal of Modern Physics, 15, 1045-1067. https://doi.org/10.4236/jmp.2024.157045
[29]
Dabass, J., Hanmandlu, M. and Vig, R. (2020) Classification of Digital Mammograms Using Information Set Features and Hanman Transform Based Classifiers. InformaticsinMedicineUnlocked, 20, Article ID: 100401. https://doi.org/10.1016/j.imu.2020.100401
[30]
Calvo, T., De Baets, B. and Fodor, J. (2001) The Functional Equations of Frank and Alsina for Uninorms and Nullnorms. FuzzySetsandSystems, 120, 385-394. https://doi.org/10.1016/s0165-0114(99)00125-6
[31]
Timofte, R., Zimmermann, K. and Van Gool, L. (2011) Multi-View Traffic Sign Detection, Recognition, and 3D Localisation. Machine Vision and Applications, 25, 633-647. https://doi.org/10.1007/s00138-011-0391-3
[32]
Temel, D., Alshawi, T., Chen, M.H. and AlRegib, G. (2019) Chal-Lenging Environments for Traffic Sign Detection: Reliability Assessment under In-Clement Conditions. arXiv: 1902.06857.
[33]
Felzenszwalb, P.F., Girshick, R.B., McAllester, D. and Ramanan, D. (2010) Object Detection with Discriminatively Trained Part-Based Models. IEEETransactionsonPatternAnalysisandMachineIntelligence, 32, 1627-1645. https://doi.org/10.1109/tpami.2009.167
