SISTEM DETEKSI MULTI-ROBOT DAN API MENGGUNAKAN IMAGE PROCESSING BERBASIS ALGORITMA YOLO

ARISTO, AHMAD FARHAN and Suprapto, Bhakti Yudho (2020) SISTEM DETEKSI MULTI-ROBOT DAN API MENGGUNAKAN IMAGE PROCESSING BERBASIS ALGORITMA YOLO. Undergraduate thesis, Sriwijaya University.

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Abstract

Accidents due to undetected fire have caused huge losses in various sectors in the world, such as office buildings, residential areas, and forest areas. This causes the need for an efficient fire detection system to increase. Image processing based on object detection system is considered capable to overcome this problem. One of the methods that are used to detect objects, and that is now developing, is the deep learning method. YOLO algorithm is a part of deep learning method. Therefore, this research will build a fire detection system with robots and fire as the objects, using YOLO-based image processing in real time. The most suitable YOLO model for the system is the Tiny YOLO VOC model, which is built with the darkflow framework. The system can detect the fire and robots with 100% accuration and a training loss value of 0.816988. The confidence value obtained to detect Robot_1 objects is 84%, Robot_2 is 92% and Fire is 88%. Thus, this research proves that the image processing system with YOLO algorithm to detect fire is a success, and can be implemented on a fire extinguisher system.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLO, Object Detection, Image Processing, Darkflow, Fire Fighter Wheeled Robot
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics > TA1632.A48 Image processing. T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics > TA1632.B35 Image processing--Digital techniques. Pattern recognition systems
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Users 5921 not found.
Date Deposited:	05 Jun 2020 03:40
Last Modified:	05 Jun 2020 03:40
URI:	http://repository.unsri.ac.id/id/eprint/29695

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