Open Access
Issue |
ITM Web Conf.
Volume 65, 2024
International Conference on Multidisciplinary Approach in Engineering, Technology and Management for Sustainable Development: A Roadmap for Viksit Bharat @ 2047 (ICMAETM-24)
|
|
---|---|---|
Article Number | 03009 | |
Number of page(s) | 9 | |
Section | Computer Engineering and Information Technology | |
DOI | https://doi.org/10.1051/itmconf/20246503009 | |
Published online | 16 July 2024 |
- R.E. Khoury, and N. Nasrallah, eds., “Intelligent Systems, Business, and Innovation Research,” 1st ed. 2024 edition, Springer, 2024. [CrossRef] [Google Scholar]
- R.N. Shaw, P. Siano, S. Makhilef, A. Ghosh, and S.L. Shimi, eds., “Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2023, Volume 1,” Springer Nature, Singapore, 2024. doi:10.1007/978-981-99-8289-9. [Google Scholar]
- L. Yu, and S. Liu, “A single-stage deep learning based approach for real-time license plate recognition in smart parking system,” International Journal of Advanced Computer Science and Applications (IJACSA), 14 (9) (2023). doi:10.14569/IJACSA.2023.01409119. [Google Scholar]
- Quadri, A. Kumar, T. Sahu, P. Kumar, and D.S. K Rakesh, “IOT based car parking system,” (2023). doi:10.2139/ssrn.4411592. [Google Scholar]
- Biyik, Z. Allam, G. Pieri, D. Moroni, M. O’Fraifer, E. O’Connell, S. Olariu, and M. Khalid, “Smart parking systems: reviewing the literature, architecture and ways forward,” Smart Cities, 4 (2) 623–642 (2021). doi:10.3390/smartcities4020032. [CrossRef] [Google Scholar]
- N. Arhab, M. Oussalah, H. Kokkonen, and A. Ollakka, “Analysis of car parking industry from social community perspective,” Soc. Netw. Anal. Min., 12 (1) 162 (2022). doi:10.1007/s13278-022-00981-x. [CrossRef] [Google Scholar]
- G. Amato, F. Carrara, F. Falchi, C. Gennaro, and C. Vairo, “Car parking occupancy detection using smart camera networks and Deep Learning,” in: 2016 IEEE Symposium on Computers and Communication (ISCC), 2016: pp. 1212–1217. doi:10.1109/ISCC.2016.7543901. [Google Scholar]
- J. Naranjo, M. Sotelo, C. Gonzalez, R. Garcia, and T. Pedro, “Using fuzzy logic in automated vehicle control,” IEEE Intell. Syst., 22 (1) 36–45 (2007). doi:10.1109/MIS.2007.18. [CrossRef] [Google Scholar]
- K. Wang, Z. Yu, S. Guan, X. Yang, M. Sheng, and Z. Tang, “Research and implementation of automatic fuzzy garage parking system based on fpga,” MATEC Web Conf., 75 07004 (2016). doi:10.1051/matecconf/20167507004. [CrossRef] [EDP Sciences] [Google Scholar]
- M. Gao, Y. Wei, Y. He, D. Zhang, Y. Tian, B. Huang, and C. Zheng, “Fuzzy controller-based design and simulation of an automatic parking system,” Journal of Software Engineering and Applications, 16 (9) 505–520 (2023). doi:10.4236/jsea.2023.169025. [CrossRef] [Google Scholar]
- M.C. Leu and Tea-Quin Kim, “Cell mapping based fuzzy control of car parking,” in: Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), IEEE, Leuven, Belgium, 1998: pp. 2494–2499. doi:10.1109/ROBOT.1998.680716. [Google Scholar]
- R. Kirtibhai Patel, and P. Meduri, “Faster R-CNN based Automatic Parking Space Detection,” in: Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence, Association for Computing Machinery, New York, NY, USA, 2020: pp. 105–109. doi:10.1145/3426826.3426846. [Google Scholar]
- K. Choeychuen, “Available car parking space detection from webcam by using adaptive mixing features,” in: 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE), 2012: pp. 12–16. doi:10.1109/JCSSE.2012.6261917. [Google Scholar]
- Y. Yuldashev, M. Mukhiddinov, A.B. Abdusalomov, R. Nasimov, and J. Cho, “Parking lot occupancy detection with improved mobilenetv3,” Sensors (Basel), 23 (17) 7642 (2023). doi:10.3390/s23177642. [CrossRef] [Google Scholar]
- Q. An, H. Wang, and X. Chen, “EPSDNet: efficient campus parking space detection via convolutional neural networks and vehicle image recognition for intelligent human–computer interactions,” Sensors, 22 (24) 9835 (2022). doi:10.3390/s22249835. [CrossRef] [Google Scholar]
- Faheem, S.A. Mahmud, G.M. Khan, M. Rahman, and H. Zafar, “A survey of intelligent car parking system,” Journal of Applied Research and Technology. JART, 11 (5) 714–726 (2013). doi:10.1016/S1665-6423(13)71580-3. [CrossRef] [Google Scholar]
- P. Sharmila, P. Rohinth, P. Priyadarshan, and G. Sarvesh, “Advanced Car Parking System,” in: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2022: pp. 1–5. doi:10.1109/ICPECTS56089.2022.10047820. [Google Scholar]
- Fahim, M. Hasan, and M.A. Chowdhury, “Smart parking systems: comprehensive review based on various aspects,” Heliyon, 7 (5) e07050 (2021). doi:10.1016/j.heliyon.2021.e07050. [CrossRef] [MathSciNet] [Google Scholar]
- Ram, “Car parking system using iot and ai,” (2022). doi:10.2139/ssrn.4273060. [Google Scholar]
- S. Rafique, S. Gul, K. Jan, and G.M. Khan, “Optimized real-time parking management framework using deep learning,” Expert Systems with Applications, 220 119686 (2023). doi:10.1016/j.eswa.2023.119686. [CrossRef] [Google Scholar]
- T. Tuncer, and O. Yar, “Fuzzy logic-based smart parking system,” ISI, 24 (5) 455–461 (2019). doi:10.18280/isi.240501. [CrossRef] [Google Scholar]
- K.A. Sunitha, K. Prema, G.S. Deepthi, E.J.E. Belinda, and N.S. Kumar, “Fuzzy based automatic multi-level vehicle parking using lab view,” in: Frontiers in Automobile and Mechanical Engineering -2010, 2010: pp. 363–367. doi:10.1109/FAME.2010.5714860. [CrossRef] [Google Scholar]
- C.M. Sánchez, M.S. Peñas, and L.G. Salvador, “A Fuzzy Decision System for an Autonomous Car Parking,” in: J. Lu, L.C. Jain, G. Zhang (Eds.), Handbook on Decision Making: Vol 2: Risk Management in Decision Making, Springer, Berlin, Heidelberg, 2012: pp. 237–258. doi:10.1007/978-3-642-25755-1_13. [CrossRef] [Google Scholar]
- D.-L. Nguyen, X.-T. Vo, A. Priadana, and K.-H. Jo, “Car detection for smart parking systems based on improved yolov5,” Vietnam J. Comp. Sci., 11 (02) 195–209 (2024). doi:10.1142/S2196888823500185. [CrossRef] [Google Scholar]
- S. Hussain, K.-B. Lee, M. A. Ahmed, B. Hayes, and Y.-C. Kim, “Two-stage fuzzy logic inference algorithm for maximizing the quality of performance under the operational constraints of power grid in electric vehicle parking lots,” Energies, 13 (18) 4634 (2020). doi:10.3390/en13184634. [CrossRef] [Google Scholar]
- M.M. Rashid, M.M. Rahman, M.R. Islam, O.N. Alwahedy, and A. Abdullahi, “Autonomous 4wd smart car parallel self-parking system by using fuzzy logic controller,” American International Journal of Sciences and Engineering Research, 2 (2) 1–31 (2019). doi:10.46545/aijser.v2i2.54. [CrossRef] [Google Scholar]
- Z.-J. Wang, J.-W. Zhang, Y.-L. Huang, H. Zhang, and A.S. Mehr, “Application of fuzzy logic for autonomous bay parking of automobiles,” Int. J. Autom. Comput., 8 (4) 445–451 (2011). doi:10.1007/s11633-011-0602-4. [CrossRef] [Google Scholar]
- Dehghani, and A. Soltani, “Site selection of car parking with the gis-based fuzzy multi-criteria decision making,” (2023). doi:10.2139/ssrn.4602529. [Google Scholar]
- D. Neupane, A. Bhattarai, S. Aryal, M.R. Bouadjenek, U. Seok, and J. Seok, “Shine: a deep learning-based accessible parking management system,” Expert Systems with Applications, 238 122205 (2024). doi:10.1016/j.eswa.2023.122205. [CrossRef] [Google Scholar]
- J. Bhattacharyya, “Step by step guide to object detection using roboflow,” Analytics India Magazine, (2020). https://analyticsindiamag.com/step-by-step-guide-to-object-detection-using-roboflow/ (accessed April 11, 2024). [Google Scholar]
- M. Sohan, T. Sai Ram, and Ch.V. Rami Reddy, “A Review on YOLOv8 and Its Advancements,” in: I.J. Jacob, S. Piramuthu, P. Falkowski Gilski (Eds.), Data Intelligence and Cognitive Informatics, Springer Nature, Singapore, 2024: pp. 529–545. doi:10.1007/978-981-99-7962-2_39. [CrossRef] [Google Scholar]
- Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An Imperative Style, HighPerformance Deep Learning Library,” in: Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019. https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html (accessed April 11, 2024). [Google Scholar]
- L. Z.S. Sudar, J.L. Imbenay, I. Budi, A. Ramadiah, P.K. Putra, and A.B. Santoso, “Textual analysis for public sentiment toward national police using crisp-dm framework,” Revue d’Intelligence Artificielle, 38 (1) 63–72 (2024). doi:10.18280/ria.380107. [CrossRef] [Google Scholar]
- Ultralytics, “YOLO performance metrics,” (n.d.). https://docs.ultralytics.com/guides/yolo-performance-metrics (accessed April 11, 2024). [Google Scholar]
- L. Rokach, “Incorporating Fuzzy Logic in Data Mining Tasks,” in: Encyclopedia of Artificial Intelligence, IGI Global, 2009: pp. 884–891. doi:10.4018/978-1-59904-849-9.ch131 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.