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Smart city article details

Title	A Method For Finding Distance In Real-Time Car Detection Through Object Detection
ID_Doc	2560
Authors	Martinelli F.; Mercaldo F.; Santone A.
Year	2024
Published	Procedia Computer Science, 246, C
DOI	http://dx.doi.org/10.1016/j.procs.2024.09.656
Abstract	The rapid evolution of deep learning techniques, applied in the smart cities context, has revolutionized computer vision applications, with particular significance in the field of object detection. In this paper, we explore the application of deep learning for real-time car detection in visual data, such as images or video streams. The proposed deep learning model is trained on large-scale datasets containing diverse images of cars, encompassing various lighting conditions, weather patterns, and traffic scenarios. Moreover, once the presence of car(s) is detected, we consider finding the distance between the detected car(s) and the camera: in this way, it is possible to understand the distance to the vehicle to take a certain countermeasure (for example braking or slowing down). The experimental analysis, performed on a dataset composed of 25532 different images confirms the effectiveness of the proposed method for real-world car detection. Moreover, examples of the distance computed between the camera and the detected cars are shown to provide samples of the adoption of the proposed method in the real-world environment. © 2024 The Authors.
Author Keywords	automotive; car detection; deep learning; smart cities; YOLO

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