Smart City Gnosys

Smart city article details

Title	Enhanced Unet3+ With Attention Mechanism And Its Purpose In Intelligent Traffic Management
ID_Doc	23692
Authors	Saini A.; Singh S.; Aswal A.S.; Agrawal A.
Year	2024
Published	2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/ICAIQSA64000.2024.10882383
Abstract	The rapid expansion of urban areas demands advanced solutions for intelligent traffic management to enhance the efficiency and safety of transportation networks. This paper introduces an enhanced UNet3+ architecture augmented with an attention mechanism, tailored for high-resolution image segmentation in traffic environments. The proposed model leverages the attention mechanism to prioritize critical features within the input data, leading to more accurate segmentation of vehicles, road infrastructure, and other relevant elements in complex urban scenes. Experimental results on benchmark traffic datasets demonstrate that the enhanced UNet3+ model significantly outperforms conventional methods, delivering superior accuracy and robustness in diverse traffic scenarios. The integration of this advanced segmentation approach into intelligent traffic systems promises to improve real-time traffic monitoring, optimize flow management, and reduce congestion. The findings underscore the potential of deploying deep learning-based segmentation models in smart city initiatives, driving the development of more adaptive and resilient traffic management solutions. © 2024 IEEE.
Author Keywords	Attention Mechanism; deep learning; Image Segmentation; Intelligent Traffic Management; Smart City Initiatives; Traffic Monitoring; U-Net3+

Similar Articles

Id	Similarity	Authors	Title	Published
43521	0.876	Shokri D.; Larouche C.; Homayouni S.	Proposing An Efficient Deep Learning Algorithm Based On Segment Anything Model For Detection And Tracking Of Vehicles Through Uncalibrated Urban Traffic Surveillance Cameras	Electronics (Switzerland), 13, 14 (2024)
18091	0.875	Santhiya P.; Jebadurai I.J.; Paulraj G.J.L.; Jenefa A.; Karan S.K.; Naveen.v E.	Deep Vision: Lane Detection In Its: A Deep Learning Segmentation Perspective	Proceedings - 2024 2nd International Conference on Inventive Computing and Informatics, ICICI 2024 (2024)
8561	0.875	Elov B.; Dauletov A.; Sucharitha Y.; Khalilova F.; Latipova M.; Abdullayeva M.	An Intelligent Traffic Management Of Vehicles Using Deep Learning Approach In Smart Cities	3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025 (2025)
32606	0.871	Kumar A.; Ranjan R.	Intelligent Traffic Identification System Powered Byconvolutional Neural Networks	ACM International Conference Proceeding Series (2023)
15433	0.869	Bhasin S.; Saini S.; Gupta D.; Mann S.	Computer Vision Based Traffic Monitoring System	2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024 (2024)
51592	0.865	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
17980	0.862	Bharaty K.S.; Konduri P.S.R.	Deep Learning-Driven Smart Signal Systems For Advanced Image And Video Processing In Urban Infrastructure	Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025 (2025)
51616	0.86	Naveen A.P.; Monisha M.C.; Shanmugapriyan T.; Geetha B.G.	Smart Transportation Revolution: Adaptive Monitoring With Squeeze And Excitation Networks	International Conference on Advanced Computing Technologies, ICoACT 2025 (2025)
50572	0.86	Adewopo V.A.; Elsayed N.	Smart City Transportation: Deep Learning Ensemble Approach For Traffic Accident Detection	IEEE Access, 12 (2024)
58556	0.859	Mane D.; Bidwe R.; Zope B.; Ranjan N.	Traffic Density Classification For Multiclass Vehicles Using Customized Convolutional Neural Network For Smart City	Lecture Notes in Networks and Systems, 461 (2022)