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A platform for research: civil engineering, architecture and urbanism
Drone Based Fire Detection System Based on Convolutional Neural Network
Open fires are happening more and more throughout Malaysia. It is either intentional or accidental fire. The most dangerous is an accidental fire because it may not be detected by anyone until it becomes large. Detecting a fire is not an easy task. People may not see an ongoing fire because it may be too far away, or the fire may be too small. The objective of this project is to build a fire detection system. Fire detecting systems are developed to ensure more accurate fire detection. To ensure accurate fire detection, this project uses a waterfall methodology. This project uses drones as a tool to help with fire detection. Using a Convolutional Neural Network (CNN), this project implements the use of the PyTorch framework in detecting fires. The testing was done with a distance of 2 meters from the fire and a height of 2 meter from the ground. Edited images were used and uploaded to the system. Accuracy results of 80% can ensure accurate fire detection. To evaluate the system, edited fire images are used to ensure the accuracy of the system. Therefore, CNN is a good tool for detecting fires.
Drone Based Fire Detection System Based on Convolutional Neural Network
Open fires are happening more and more throughout Malaysia. It is either intentional or accidental fire. The most dangerous is an accidental fire because it may not be detected by anyone until it becomes large. Detecting a fire is not an easy task. People may not see an ongoing fire because it may be too far away, or the fire may be too small. The objective of this project is to build a fire detection system. Fire detecting systems are developed to ensure more accurate fire detection. To ensure accurate fire detection, this project uses a waterfall methodology. This project uses drones as a tool to help with fire detection. Using a Convolutional Neural Network (CNN), this project implements the use of the PyTorch framework in detecting fires. The testing was done with a distance of 2 meters from the fire and a height of 2 meter from the ground. Edited images were used and uploaded to the system. Accuracy results of 80% can ensure accurate fire detection. To evaluate the system, edited fire images are used to ensure the accuracy of the system. Therefore, CNN is a good tool for detecting fires.
Drone Based Fire Detection System Based on Convolutional Neural Network
Rahman, Hanif Ikmal (author) / Saad, Ahmad Fadli (author) / Yani, Achmad (author)
2024-06-25
International Journal of Artificial Intelligence; Vol 11 No 1: June 2024; 26-36 ; 2686-3251 ; 2407-7275 ; 10.36079/lamintang.ijai-01101
Article (Journal)
Electronic Resource
English
DDC:
624