Smart Home Security System Using Object Recognition with the EfficientDet Algorithm: A Real-Time Approach

The EfficientDet method, which is implemented on the Raspberry Pi for real-time detection in resource-constrained contexts, is the basis for the smart home security system presented in this study. The system integrates CCTV cameras, motion sensors, and detectors to identify and classify objects, sending notifications via WhatsApp via the Twilio API. The EfficientDet-D0 model achieves an accuracy of 94.8%, an average processing time of 45 ms, and a memory usage of about 850 MB. When compared to moving individuals or non-human things, testing shows that stationary human items have a higher detection accuracy. Notifications are transmitted roughly every three seconds, with an average latency of 1.4 to 1.8 seconds. The suggested method provides object recognition, real-time monitoring, and configuration flexibility in contrast to traditional IoT-based systems. These results highlight the potential of EfficientDet as a reliable and adaptable solution for home security. Future improvements include improving accuracy in a variety of environmental conditions and implementing adaptive learning.

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Publication Details

Journal
International Journal of Engineering Continuity
Published
2025-08-15
DOI
https://doi.org/10.58291/ijec.v4i1.400
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Smart Home Security System Using Object Recognition with the EfficientDet Algorithm: A Real-Time Approach

Nor Maniha Abdul Ghani, Nurwan Reza Fachrur Rozi, Yus Natali, Pietra Dorand et al.
International Journal of Engineering Continuity
IoT-based Smart Home Systems
article

Smart Home Security System Using Object Recognition with the EfficientDet Algorithm: A Real-Time Approach

Nor Maniha Abdul Ghani, Nurwan Reza Fachrur Rozi, Yus Natali, Pietra Dorand, Suyatno Suyatno, Muhamad Roihan
article en

Abstract

The EfficientDet method, which is implemented on the Raspberry Pi for real-time detection in resource-constrained contexts, is the basis for the smart home security system presented in this study. The system integrates CCTV cameras, motion sensors, and detectors to identify and classify objects, sending notifications via WhatsApp via the Twilio API. The EfficientDet-D0 model achieves an accuracy of 94.8%, an average processing time of 45 ms, and a memory usage of about 850 MB. When compared to moving individuals or non-human things, testing shows that stationary human items have a higher detection accuracy. Notifications are transmitted roughly every three seconds, with an average latency of 1.4 to 1.8 seconds. The suggested method provides object recognition, real-time monitoring, and configuration flexibility in contrast to traditional IoT-based systems. These results highlight the potential of EfficientDet as a reliable and adaptable solution for home security. Future improvements include improving accuracy in a variety of environmental conditions and implementing adaptive learning.

International Journal of Engineering ContinuityVol. 4(1)
Telkom University (ID)
Universitas Telkom
Openalex Percentile: Top 80%
IoT-based Smart Home Systems
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