PENDETEKSIAN KEMUNCULAN OBJEK NYALA API BERBASISKAN CITRA DIGITAL BERGERAK

MAULANA, ARAS and Fachrurrozi, Muhammad (2022) PENDETEKSIAN KEMUNCULAN OBJEK NYALA API BERBASISKAN CITRA DIGITAL BERGERAK. Undergraduate thesis, Sriwijaya University.

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Abstract

Object detection is required to prevent fire disaster. Object detection is a famous development in computer vision area. It’s because its ability that can differentiate between object in a natural image capture. Fire detection can be applied on a device such as home surveillance camera. The function that being utilize is the ability to detect object around it. Object detection in this research is YOLO method. YOLO or You Only Look Once is a detection method based on neural networks. YOLO does the detection process through CNN algorithm only once. This research chose YOLOv3 as the YOLO version used. This research use a primary type dataset with a total of 4 datasets. The video format is mp4. The dataset is divided by 2 parts, one 5 second video for the training process, and 3 1 second video for the testing process. This research found the result which show 48% of dataset 1 are able to achieve a satisfying result, while dataset 2 only able to achieve 3%. The system failed to detect any object in dataset 3. The failure is due to dataset lack of quantity and the data variety in training process.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLO, Deteksi Objek, Computer vision
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 25955 not found.
Date Deposited:	24 Nov 2022 02:35
Last Modified:	24 Nov 2022 02:35
URI:	http://repository.unsri.ac.id/id/eprint/82622

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