Text

DETEKSI KEBAKARAN BERDASARKAN VIDEO MENGGUNAKAN MASK R-CNN

---

Penilaian

---

Fire is one of the disasters that can cause great losses, both in terms of loss of life and economic loss. Fast and accurate fire detection is the main key to prevent fires from getting bigger and out of control. This study aims to develop a fire detection system using a video camera that can provide fire information as quickly as possible. This fire detection system uses Mask R-CNN model to detect fire objects in each frame captured by the video camera. The Mask R-CNN model was trained using 53184 images containing fire objects and objects that resemble fire. This image data was obtained from the results of augmenting a dataset consisting of 2216 images. The augmentation performed is an image rotation of 15 degrees from 0 to 360 degrees, so that the data produced after augmentation is 24 times larger. The results of the study showed that the developed model was able to detect fires with an AP50 of 77,87%. This result was obtained from several model experiments that produced the best model with a ResNet 101 backbone, 1000 number of proposals, and a base learning rate of 5 x 10^-4.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002780	T173265	T1732652025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1732652025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2025

Collation

vi, 91 hlm.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

EM

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment