1. A comparative study on faster R-CNN, YOLO and SSD object detection algorithms on HIDS system.
- Author
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Sarma, K. S. R. K., Sasikala, Chinthakunta, Surendra, Katepogu, Erukala, Sudarshan, and Aruna, S. L.
- Subjects
OBJECT recognition (Computer vision) ,SOLID state drives ,CONVOLUTIONAL neural networks ,HOME security measures ,ALGORITHMS ,WIRELESS Internet - Abstract
It takes time, money, and effort to keep a watch on your home. Numerous catastrophes, such as burglaries and vandalism, had a place in homes when the owners were negligent or absent. While employing staff is not thought to be a cost-effective alternative, some residential areas employ guards to watch over their homes. A mobile application-based Internet of Things (IoT) system called Home Intruder Detection System (HIDS) assists homeowners in keeping an eye on their houses by remotely alerting users to any potential dangers. The main goals of HIDS are to develop a trustworthy home security system using IoT, to apply the object detection algorithm to identify human presence, and to develop an intelligent mobile application that will allow users to monitor their homes from anywhere in the world and receive alerts if any threats are found. To identify intruders using a camera attached to the system, HIDS is used tested and compared the Region-based Convolution Neural Network (R-CNN), Single-Shot Multibox Detection (SSD) and You Only Look Once (YOLO) detection object detection method in the NVIDIA Jetson Nano. The system is capable of delivering detection video to the server and capturing video at frame rate (FPS) of 44.25 for YOLO, 37.81 for SSD and 27.21 for faster R-CNN respectively. Faster R-CNN, SSD, and YOLO algorithms' average precisions will be 94.08%, 89.75%, and 81.92% respectively. HIDS achieves its objectives by successfully recognizing people and remotely informing detection users via mobile applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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