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Title	Deep Learning Algorithms For Object Detection In Smart Environments
ID_Doc	17803
Authors	Dhanya D.; Jasmine R.R.; Kokila M.L.S.; Sakthivel M.; Divya N.; Boopathi S.
Year	2025
Published	Navigating Challenges of Object Detection through Cognitive Computing
DOI	http://dx.doi.org/10.4018/979-8-3693-9057-3.ch004
Abstract	This chapter has presented the deep learning algorithms in smart environments for object detection purpose; it has, however, come to bode to broader introspect towards more enhanced automation and intelligence. These include advanced architectures of CNN, Region-based CNN, and YOLO which have accomplished their efficiencies in pursuance both for real-time object identification and their subsequent tracking. These offer high accuracy and multiple applications in the smart home, smart city and Industrial IoT, leveraging large datasets and complex computational power. Discussion The discussion addresses challenges in computing complexity, energy efficiency, and model scalability in resource-impoverished environments. Therefore, important case studies and practical examples would describe the ways in which these alleys can be combined with sensor networks and IoT systems, especially the opportunity to revolutionize areas such as security, automation, and adaptable resource management. © 2025, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited.
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