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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Faruque, Gazi Golam
Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Sirajganj-6751, Bangladesh
Computer Science & IT Electrical & Electronics Engineering

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Smart Security Solution for Market Shop Using IoT and Deep Learning Bin Abdul Hai, Talha; Rahman, Wahidur; Hosen, Md Solaiman; Islam, Md. Tarequl; Sadi, A H M Saifullah; Faruque, Gazi Golam; Azad, Mir Mohammad
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i1.4780

Abstract

Nowadays, security system in the market shop is an immense concern everywhere. The modern world is leaning towards intelligent, automated security systems instead of the traditional human-based security or CCTV surveillance system because of their limitations. A typical CCTV surveillance system is not intelligent enough to detect intruders or fire. The proposed security system in this paper is an IoT, deep learning, and GSM based innovative security solution specially designed for shops and offices. The objectives of this system are to prevent burglary and fire. For this, the proposed model focuses on fire and intruder detection through both IoT and deep learning approaches. Several IoT sensors have been utilized with a deep learning model to detect fires in shops or offices at an initial stage. The model also utilizes a current sensor for identifying electrical short-circuit to prevent unexpected damages. This system further utilizes GSM technology to send the corresponding notifications to the authorized user and play alarm sounds at the shop as well as the owner's house while detecting suspicious occurrences. The proposed solution has used two pre-trained Convolutional Neural Network (CNN) architecture, namely ResNet50 and Inception V3. This research found an accuracy of 99.49% with ResNet50 architecture in fire detection.
Co-Authors Azad, Mir Mohammad Bin Abdul Hai, Talha Hosen, Md Solaiman Islam, Md. Tarequl Rahman, Wahidur Sadi, A H M Saifullah