Citation

Rajapriyan VS, Harsha V, Kaaran R, Remya V. Int. J. Eng. Technol. Manage. Sci. 2022; 6(4): 266-271.

Copyright

(Copyright © 2022, International Journal of Engineering Technology and Management Sciences)

DOI

10.46647/ijetms.2022.v06i04.0045

PMID

unavailable

Abstract

Fire detection systems play a key role in industries, shops, malls, etc. in detecting fire at the initial stages and help in saving lives and property. Commercial fire detecting systems usually have an alarm signaling with a buzzer, which has been found inadequate. To overcome these deficiencies, this project aims to design a fire detection system. In the proposed model YOLO v3 is used to detect the fire in images. Initially, it will train the dataset and after the training, the trained model is used for further process. The user has an option to upload the image and click submit, after this request hitting on the backend endpoint, identification of features will be done and soon the output will be shown saying that the image has fired or not. Also, if the fire is detected, an email and the location will also be sent to the given email address. There are quite a few such existing systems, however, they are not as effective as the situation demands to need. So a new system to detect fire from infrared images have proposed. By making use of computer vision and machine learning techniques to make it efficient and reliable. Also, the system uses brightness classification along with image processing and histogram-based segmentation. All these are made to increase the accuracy and make the system more suitable for real-time implementation. Thus, the proposed system not only solves the existing problems but also provides a new and efficient approach. Here YOLO V3 is used as a classifier to classify based on the parameters learned during the training process and the binary cross-entropy loss function is used to figure out how good our model is. Accuracy is used as an evaluation metric. Hence the main goal of the project is to accurately detect the presence of the fire as early as possible.

Language: en