Deep Learning-Based Road Traffic Density Analysis and Monitoring Using Semantic Segmentation
Bilah Samping Artikel
Isi Artikel Utama
Abstrak
Due to factors such as a growing population, more people using private vehicles, and outdated transportation infrastructure, Jakarta, the capital city of Indonesia, suffers from chronic traffic congestion. The environment, citizens' safety, productivity, and quality of life are all negatively impacted by these interruptions. In response to these difficulties, this study proposes a novel method for traffic monitoring. By combining YOLOv5, optical flow, and recurrent neural networks (RNN) with image processing and artificial neural networks, a unified traffic monitoring system can be achieved. We went with YOLOv5 because of how well it identifies various automobiles. The number of vehicles is counted between video frames using Optical Flow, and then the traffic density is classified using RNN. With an accuracy of 87% following testing, RNN was clearly a winner when it came to vehicle density classification. The goals of this research are to lessen the societal and environmental toll of traffic congestion, increase our knowledge of and ability to control Jakarta's traffic, and lay the groundwork for the creation of more advanced traffic monitoring systems. The growing traffic issues in the nation's capital are anticipated to be alleviated with this strategy.
Rincian Artikel
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
Referensi
K. Khazukov et al., (2020), “Real-time monitoring of traffic parameters,” Journal of Big Data, vol. 7, no. 1, pp. 1–20, doi:10.1186/S40537-020-00358-X/FIGURES/21. DOI: https://doi.org/10.1186/s40537-020-00358-x
Y. Jiao, G. Shi, and T. D. Tran, (2021), “Optical Flow Estimation Via Motion Feature Recovery,” in Proceedings - International Conference on Image Processing, ICIP, pp. 2558–2562. DOI: https://doi.org/10.1109/ICIP42928.2021.9506523
S. Ando and T. Kindo, (2022), “Direct Imaging of Stabilized Optical Flow and Possible Anomalies from Moving Vehicle,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 24044–24056, doi:10.1109/TITS.2022.3199203. DOI: https://doi.org/10.1109/TITS.2022.3199203
J. Yuan, T. Jiang, X. He, S. Wu, J. Liu, and D. Guo, (2023), “Dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving,” Scientific Reports, vol. 13, no. 1, pp. 1–10, doi:10.1038/s41598-023-34777-6. DOI: https://doi.org/10.1038/s41598-023-34777-6
Y. Fan et al., (2022), “Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection,” Electronics, vol. 11, no. 15, pp. 1–20, doi:10.3390/ELECTRONICS11152344. DOI: https://doi.org/10.3390/electronics11152344
M. Kasper-Eulaers, N. Hahn, P. E. Kummervold, S. Berger, T. Sebulonsen, and Ø. Myrland, (2021), “Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5,” Algorithms, vol. 14, no. 4, pp. 1–11, doi:10.3390/A14040114. DOI: https://doi.org/10.3390/a14040114
L. Shao, H. Wu, C. Li, and J. Li, (2023), “A Vehicle Recognition Model Based on Improved YOLOv5,” Electronics, vol. 12, no. 6, pp. 1–14, doi:10.3390/ELECTRONICS12061323. DOI: https://doi.org/10.3390/electronics12061323
Y. Cai, L. Dai, H. Wang, L. Chen, and Y. Li, (2022), “DLnet With Training Task Conversion Stream for Precise Semantic Segmentation in Actual Traffic Scene,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6443–6457, doi:10.1109/TNNLS.2021.3080261. DOI: https://doi.org/10.1109/TNNLS.2021.3080261
S. Lu, Z. Luo, F. Gao, M. Liu, K. Chang, and C. Piao, (2021), “A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation,” Sensors, vol. 21, no. 2, p. 400, doi:10.3390/S21020400. DOI: https://doi.org/10.3390/s21020400
S. Liu, J. Cheng, L. Liang, H. Bai, and W. Dang, (2021), “Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8287–8296, doi:10.1109/JSTARS.2021.3104382. DOI: https://doi.org/10.1109/JSTARS.2021.3104382
H. Ghandorh, W. Boulila, S. Masood, A. Koubaa, F. Ahmed, and J. Ahmad, (2022), “Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images,” Remote Sensing, vol. 14, no. 3, p. 613, doi:10.3390/RS14030613. DOI: https://doi.org/10.3390/rs14030613
M. S, N. P, and M. S. S, (2021), “Instance Segmentation for Autonomous Vehicle,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 9, pp. 565–570.
T. H. Wu, T. W. Wang, and Y. Q. Liu, (2021), “Real-Time Vehicle and Distance Detection Based on Improved Yolo v5 Network,” 2021 3rd World Symposium on Artificial Intelligence, WSAI 2021, pp. 24–28, doi:10.1109/WSAI51899.2021.9486316. DOI: https://doi.org/10.1109/WSAI51899.2021.9486316
Z. Chen, L. Cao, and Q. Wang, (2022), “YOLOv5-Based Vehicle Detection Method for High-Resolution UAV Images,” Mobile Information Systems, vol. 2022, doi:10.1155/2022/1828848. DOI: https://doi.org/10.1155/2022/1828848
A. Li, S. Sun, Z. Zhang, M. Feng, C. Wu, and W. Li, (2023), “A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5,” Electronics, vol. 12, no. 4, pp. 1–16, doi:10.3390/ELECTRONICS12040878. DOI: https://doi.org/10.3390/electronics12040878
N. Al-Qubaydhi et al., (2022), “Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning,” Electronics, vol. 11, no. 17, pp. 1–18, doi:10.3390/ELECTRONICS11172669. DOI: https://doi.org/10.3390/electronics11172669
K. Blachut and T. Kryjak, (2022), “Real-Time Efficient FPGA Implementation of the Multi-Scale Lucas-Kanade and Horn-Schunck Optical Flow Algorithms for a 4K Video Stream,” Sensors, vol. 22, no. 13, doi:10.3390/S22135017. DOI: https://doi.org/10.3390/s22135017
M. Yao, J. Wang, J. Peng, M. Chi, and C. Liu, (Oct. 2023), “FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow,” MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia, pp. 3375–3383, doi:10.1145/3581783.3611868. DOI: https://doi.org/10.1145/3581783.3611868
S. Shen, L. Kerofsky, and S. Yogamani, (2023), “Optical Flow for Autonomous Driving: Applications, Challenges and Improvements,” ArXiv, doi:10.48550/ARXIV.2301.04422.
J. M. Ackerson, R. Dave, and J. Seliya, (2021), “Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection,” Information, vol. 12, no. 7, doi:10.3390/INFO12070272. DOI: https://doi.org/10.3390/info12070272
U. Mittal and P. Chawla, (Jan. 2023), “Acoustic Based Emergency Vehicle Detection Using Ensemble of deep Learning Models,” Procedia Computer Science, vol. 218, pp. 227–234, doi:10.1016/J.PROCS.2023.01.005. DOI: https://doi.org/10.1016/j.procs.2023.01.005
I. Ullah and Q. H. Mahmoud, (2022), “Design and Development of RNN Anomaly Detection Model for IoT Networks,” IEEE Access, vol. 10, pp. 62722–62750, doi:10.1109/ACCESS.2022.3176317. DOI: https://doi.org/10.1109/ACCESS.2022.3176317
K. Griesbach, M. Beggiato, and K. H. Hoffmann, (2022), “Lane Change Prediction With an Echo State Network and Recurrent Neural Network in the Urban Area,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6473–6479, doi:10.1109/TITS.2021.3058035. DOI: https://doi.org/10.1109/TITS.2021.3058035
G. Kocher and G. Kumar, (2021), “Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges,” Soft Computing 2021 25:15, vol. 25, no. 15, pp. 9731–9763, doi:10.1007/S00500-021-05893-0. DOI: https://doi.org/10.1007/s00500-021-05893-0
Y. Jeong, S. Kim, and K. Yi, (2020), “Surround vehicle motion prediction using lstm-rnn for motion planning of autonomous vehicles at multi-lane turn intersections,” IEEE Open Journal of Intelligent Transportation Systems, vol. 1, no. 1, pp. 2–14, doi:10.1109/OJITS.2020.2965969. DOI: https://doi.org/10.1109/OJITS.2020.2965969
G. Cheng and J. Y. Zheng, (2020), “Semantic segmentation for pedestrian detection from motion in temporal domain,” Proceedings - International Conference on Pattern Recognition, pp. 6897–6903, doi:10.1109/ICPR48806.2021.9411958. DOI: https://doi.org/10.1109/ICPR48806.2021.9411958
M. Imad, O. Doukhi, and D. J. Lee, (2021), “Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud,” Sensors, vol. 21, no. 12, doi:10.3390/S21123964. DOI: https://doi.org/10.3390/s21123964
M. Colley, B. Eder, J. O. Rixen, and E. Rukzio, (2021), “Efects of semantic segmentation visualization on trust, situation awareness, and cognitive load in highly automated vehicles,” Conference on Human Factors in Computing Systems - Proceedings, doi:10.1145/3411764.3445351/SUPPL_FILE/3411764.3445351_VIDEOFIGURE.MP4. DOI: https://doi.org/10.1145/3411764.3445351
Z. Deng, C. Yao, and Q. Yin, (2023), “Safety Helmet Wearing Detection Based on Jetson Nano and Improved YOLOv5,” Advances in Civil Engineering, vol. 2023, no. 1, doi:10.1155/2023/1959962. DOI: https://doi.org/10.1155/2023/1959962
M. Liu, B. Liao, C. Wang, Y. Wang, and Y. Wang, (Nov. 2020), “Real-Time Vehicle Taillight Recognition Based on Siamese Recurrent Neural Network,” Journal of Physics: Conference Series, vol. 1673, no. 1, doi:10.1088/1742-6596/1673/1/012056. DOI: https://doi.org/10.1088/1742-6596/1673/1/012056
Y. Luo, Y. Xiao, L. Cheng, G. Peng, and D. D. Yao, (2021), “Deep Learning-based Anomaly Detection in Cyber-physical Systems,” ACM Computing Surveys (CSUR), vol. 54, no. 5, doi:10.1145/3453155. DOI: https://doi.org/10.1145/3453155