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Journal : Journal of Embedded Systems, Security and Intelligent Systems

Vehicle Detection Counting using YOLO and DeepSORT on Edge Device Rafli; Wardoyo, Siswo; Alfanz, Rocky; Fahrizal, Rian; Muhammad, Fadil; Muttakin, Imamul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9482

Abstract

Vehicle counting is a crucial method used in traffic management. Computer vision can be employed for efficient detection and classification techniques for vehicle objects. This paper reports on a simultaneous process of vehicle classification and counting implemented on NVIDIA Jetson Nano. The use of YOLOv5 overcomes computational load issues in edge computing deployments, whereas its combination with the DeepSORT tracker algorithm enhances the accuracy of vehicle detection and counting in various directions. A total of 18200 images are used to train the detectors that are designed to target local vehicles. The average accuracy of the model for detecting cars, motorcycles, buses, and trucks is 72.1%, 21.56%, 70%, and 25.63%, respectively. Real-time tests obtained an overall average vehicle counting accuracy of 49.95%.