Smart City Gnosys

Smart city article details

Title	Vehicle Detection And Classification Using Yolov5 On Fused Infrared And Visible Images
ID_Doc	60904
Authors	Shihabudeen H.; Rajeesh J.
Year	2023
Published	6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings
DOI	http://dx.doi.org/10.1109/ICICT57646.2023.10134214
Abstract	Visual surveillance systems in intelligent transportation systems need to detect and classify vehicles in order to create an ambient environment in smart cities. The presence of data imbalances reduces the performance of these systems. The suggested model utilizes deep learning techniques for effective identification and classification of vehicles. The YOLO v5 architecture is utilised for detection and can identify very small vehicles like bikes and scooters on the road. A fusion model is designed to generate a composite image with more complementary features for detection purposes. The fusion of infrared and visible modalities can create images of improved resolution that are suitable for night-time vehicle recognition and suited for the surveillance of remote locations. The quality of the fused image was also verified with similar models, and structural similarity is very good. In comparison with similar models, the novel algorithm produces good metric values like 0.96 for accuracy, 0.97 for sensitivity, 0.97 for specificity, and 0.93 for precision. © 2023 IEEE.
Author Keywords	Classification; Deeplearning; Fusion; Infrared Image; Vehicle Detection; Visible Image; Yolov5

Similar Articles

Id	Similarity	Authors	Title	Published
765	0.903	Shokri D.; Larouche C.; Homayouni S.	A Comparative Analysis Of Multi-Label Deep Learning Classifiers For Real-Time Vehicle Detection To Support Intelligent Transportation Systems	Smart Cities, 6, 5 (2023)
62117	0.894	Tang J.; Ye C.; Zhou X.; Xu L.	Yolo-Fusion And Internet Of Things: Advancing Object Detection In Smart Transportation	Alexandria Engineering Journal, 107 (2024)
44409	0.886	Alahdal N.M.; Abukhodair F.; Meftah L.H.; Cherif A.	Real-Time Object Detection In Autonomous Vehicles With Yolo	Procedia Computer Science, 246, C (2024)
16291	0.886	Wang D.; Al-Rubaie A.; Alsarkal Y.I.; Stincic S.; Davies J.	Cost Effective And Accurate Vehicle Make/Model Recognition Method Using Yolov5	2021 International Conference on Smart Applications, Communications and Networking, SmartNets 2021 (2021)
7536	0.885	Khalid M.; Ashraf A.; Bangyal W.H.; Iqbal M.	An Android Application For Unwanted Vehicle Detection And Counting	Proceedings - 2023 Human-Centered Cognitive Systems, HCCS 2023 (2023)
13518	0.884	Ramakanth Kumar P.; Jambur P.V.; Kumar D.	Cctv Surveillance-Based Vehicle Identification: Query-Driven Search Using Colour, Manufacturer & License Plate	Proceedings of the 3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025 (2025)
54356	0.883	Padilla Carrasco D.; Rashwan H.A.; Garcia M.A.; Puig D.	T-Yolo: Tiny Vehicle Detection Based On Yolo And Multi-Scale Convolutional Neural Networks	IEEE Access, 11 (2023)
17960	0.881	Sharma T.; Debaque B.; Duclos N.; Chehri A.; Kinder B.; Fortier P.	Deep Learning-Based Object Detection And Scene Perception Under Bad Weather Conditions	Electronics (Switzerland), 11, 4 (2022)
60901	0.88	Rashmi C.R.; Shantala C.P.	Vehicle Density Analysis And Classification Using Yolov3 For Smart Cities	Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020 (2020)
39607	0.876	Du L.	Object Detectors In Autonomous Vehicles: Analysis Of Deep Learning Techniques	International Journal of Advanced Computer Science and Applications, 14, 10 (2023)