Öz
Kaynakça
- [1] Yaldiz, F. (2010). Development of image-based vehicle detection system in traffic automation and implementation with FPGA.
- [2] Jadhav, P., Kelkar, P., Patil, K., & Thorat, S. (2016). Smart traffic control system using image processing. International Research Journal of Engineering and Technology (IRJET), 3(3), 2395-0056.
- [3] Pazar, Ş., Bulut, M., & Uysal, C. (2020). Development of Artificial Intelligence Based Vehicle Detection System. Journal of Scientific, Technology and Engineering Research, 1(1), 31-37.
- [4] Tan, Z. (2019). Vehicle classification with the help of deep learning (Master's thesis, Fırat University, Institute of Science and Technology).
- [5] Alyuruk, M. (2020). Chapter 18 Intelligent Transportation Systems and the Future of Transportation. Transforming Professions and Industries in the Digital Future.
- [6] Azimjonov, J. (2021). Development of real-time vision-based traffic flow information computing systems for intersections and highways.
- [7] Bulbul, H. (2020). Real Time Vehicle Detection Using Deep Learning Methods.
- [8] Dikbayir, H. S., & Bulbul, H. I.(2020). Real Time Vehicle Detection Using Deep Learning Methods. Tubaw Science Journal, 13(3), 1-14.
- [9] Oguzhan, A.(2021) Autonomous traffic lights control and optimization, Istanbul University.
- [10] Azimjonov, J. (2021). Development of real-time vision-based traffic flow information computing systems for intersections and highways.
- [11] Tunc, I., Elmas, O., Edem, A., Koroglu, A., Akmese, S., & Soylemez, M. (2021) Traffic Light Signalling with Deep Q Learning Technique.
- [12] Krishna, B. H., Patra, P. S. K., & Kalpana, G. (2021, July). Real Time Traffic Light Controlling System Using Morphological Operators and Fuzzy Logic. In Journal of Physics: Conference Series (Vol. 1964, No. 6, p. 062061). IOP Publishing.
- [13] Nam, D., Lavanya, R., Jayakrishnan, R., Yang, I., & Jeon, W. H. (2020). A deep learning approach for estimating traffic density using data obtained from connected and autonomous probes. Sensors, 20(17), 4824.
- [14] Chandrasekara, W. A. C. J. K., Rathnayaka, R. M. K. T., & Chathuranga, L. L. G. (2020, December). A Real-Time Density-Based Traffic Signal Control System. In 2020 5th International Conference on Information Technology Research (ICITR) (pp. 1-6). IEEE.
- [15] Anas, A. M., Terzioglu, H., & Durdu, A. (2020). Intelligent Traffic Signalling Control Using Petri Nets. Artifıcial Intelligence, 3(1), 1-13.
- [16] Osman, T., Psyche, S. S., Ferdous, J. S., & Zaman, H. U. (2017, January). Intelligent traffic management system for cross section of roads using computer vision. In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 1-7). IEEE.
- [17] Khooban, M. H., Vafamand, N., Liaghat, A., & Dragicevic, T. (2017). An optimal general type-2 fuzzy controller for Urban Traffic Network. ISA transactions, 66, 335-343.
Öz
The duration of the smart intersection system lights is determined automatically according to the nearest busy. The vehicle at the intersection with the camera is calculated by the image processing process. optimizing the signaling time in traffic signaling. It will be passed to be passed by a system that can be reached later. Also the system can be entered with this remote central management. Manually switch to roads. In this study, it is a smart intersection system used with special permission from Malatya Metropolitan Municipality transportation units. These studies and the benefits they have provided are highlighted. In addition, DARKNET's real-time object detection YOLOV3 deep learning model is used within the scope of in-vehicle real-time traffic system from data images on websites for traffic. The vehicles are placed in the targeted and future-determined database. Positive signaling with information from the designed Process-Based Intersection Management System. Agricultural bounty takes advantage of little stealing gases to be grown to take advantage of time and small items. A clean environment will be created.
Anahtar Kelimeler
Smart City Smart Transportation Smart Junction Image Processing Deep Learning Darknet Yolov3
Kaynakça
- [1] Yaldiz, F. (2010). Development of image-based vehicle detection system in traffic automation and implementation with FPGA.
- [2] Jadhav, P., Kelkar, P., Patil, K., & Thorat, S. (2016). Smart traffic control system using image processing. International Research Journal of Engineering and Technology (IRJET), 3(3), 2395-0056.
- [3] Pazar, Ş., Bulut, M., & Uysal, C. (2020). Development of Artificial Intelligence Based Vehicle Detection System. Journal of Scientific, Technology and Engineering Research, 1(1), 31-37.
- [4] Tan, Z. (2019). Vehicle classification with the help of deep learning (Master's thesis, Fırat University, Institute of Science and Technology).
- [5] Alyuruk, M. (2020). Chapter 18 Intelligent Transportation Systems and the Future of Transportation. Transforming Professions and Industries in the Digital Future.
- [6] Azimjonov, J. (2021). Development of real-time vision-based traffic flow information computing systems for intersections and highways.
- [7] Bulbul, H. (2020). Real Time Vehicle Detection Using Deep Learning Methods.
- [8] Dikbayir, H. S., & Bulbul, H. I.(2020). Real Time Vehicle Detection Using Deep Learning Methods. Tubaw Science Journal, 13(3), 1-14.
- [9] Oguzhan, A.(2021) Autonomous traffic lights control and optimization, Istanbul University.
- [10] Azimjonov, J. (2021). Development of real-time vision-based traffic flow information computing systems for intersections and highways.
- [11] Tunc, I., Elmas, O., Edem, A., Koroglu, A., Akmese, S., & Soylemez, M. (2021) Traffic Light Signalling with Deep Q Learning Technique.
- [12] Krishna, B. H., Patra, P. S. K., & Kalpana, G. (2021, July). Real Time Traffic Light Controlling System Using Morphological Operators and Fuzzy Logic. In Journal of Physics: Conference Series (Vol. 1964, No. 6, p. 062061). IOP Publishing.
- [13] Nam, D., Lavanya, R., Jayakrishnan, R., Yang, I., & Jeon, W. H. (2020). A deep learning approach for estimating traffic density using data obtained from connected and autonomous probes. Sensors, 20(17), 4824.
- [14] Chandrasekara, W. A. C. J. K., Rathnayaka, R. M. K. T., & Chathuranga, L. L. G. (2020, December). A Real-Time Density-Based Traffic Signal Control System. In 2020 5th International Conference on Information Technology Research (ICITR) (pp. 1-6). IEEE.
- [15] Anas, A. M., Terzioglu, H., & Durdu, A. (2020). Intelligent Traffic Signalling Control Using Petri Nets. Artifıcial Intelligence, 3(1), 1-13.
- [16] Osman, T., Psyche, S. S., Ferdous, J. S., & Zaman, H. U. (2017, January). Intelligent traffic management system for cross section of roads using computer vision. In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 1-7). IEEE.
- [17] Khooban, M. H., Vafamand, N., Liaghat, A., & Dragicevic, T. (2017). An optimal general type-2 fuzzy controller for Urban Traffic Network. ISA transactions, 66, 335-343.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | TJST |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Eylül 2022 |
| Gönderilme Tarihi | 14 Nisan 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 17 Sayı: 2 |
