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Title Comparative Study Of Object Detection Models For Safety In Autonomous Vehicles, Homes, And Roads Using Iot Devices
ID_Doc 15067
Authors Hasan M.; Alavee K.A.; Bin Bashar S.Z.; Rahman M.T.; Hossain M.I.; Rabiul Alam M.G.
Year 2023
Published Proceedings of the 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023
DOI http://dx.doi.org/10.1109/CSDE59766.2023.10487666
Abstract Object detection plays a pivotal role in enhancing security, surveillance, and automation. It enables timely threat identification, streamlines traffic management, and facilitates efficient resource allocation. By automating object recognition, it creates value through improved safety, productivity, and resource optimization in various domains, from smart cities to industrial settings. This paper explores modern object detection methods to develop a Real-Time Responsive CCTV Camera Model. As we transition to a 5G-connected world, smart devices are poised to manage our daily security. We focus on transforming conventional CCTV cameras into responsive, internet-connected security guards. By evaluating methods like HOG, Viola-Jones, R-CNN, SSD, and YOLO, we aim to select the most efficient algorithm. We found Yolov8 perform best among all the models based on accuracy 99.8% and FPS 40. Our research strives to create cost-effective, intelligent security systems, paving the way for automated alerts and a digitally secured future. © 2023 IEEE.
Author Keywords CCTV Camera; Computer Vision; Fire; Image Processing; Modern Home Security; Object Detection; SSD; Surveillance and Security Systems; Weapon and Threat Detection; YOLO


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