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ACNN-IFD: An Improved Image Fire Detection System Based on Advanced Convolutional Neural Networks | ||
| Al-Zahraa Journal for Artificial Intelligence Applications | ||
| Article 3, Volume 0, Issue 1, September 2026, Pages 24-38 PDF (810.92 K) | ||
| Document Type: Research Article | ||
| DOI: 10.66723/zjaia.2026.163832.1006 | ||
| Author | ||
| Sara Faez Abdulghani* | ||
| AL-Zahraa University for Women, College of health and medical technology, Karbala, Iraq | ||
| Abstract | ||
| Fire detection by recognizing flames from images is an important area of current research. Early fire detection by visual means will allow quicker responses in potentially dangerous or life-threatening situations. However, traditional methods for detecting fires through images have been shown to be unreliable in complex scenes due to variations in how different types of flames appear and the possibility of similar-looking objects being present in the scene that are not actual fires. Therefore, the purpose of this study was to propose a new image fire detection system. This proposed image fire detection system includes a two-step approach combining a candidate region refinement process based upon the color features and outline features of potential flame candidates with a classification step utilizing an Advanced Convolutional Neural Network (ACNN). The proposed image fire detection system was tested using a data set available from Kaggle consisting of 999 images that include 780 images of fires and 219 images of no fires. Candidate regions of interest identified within each image were resized to 180×180 pixels and normalized prior to being inputted into the ACNN. The experimental results obtained from testing the proposed image fire detection system on the employed data set demonstrated a test accuracy of 95%. These results indicate promise in the ability of the proposed system to detect fires under the conditions tested. However, since the proposed system was validated only against the employed data set, additional testing on other data sets would need to be performed to assess its generalizability | ||
| Keywords | ||
| Image fire detection system; CNN; Deep learning; image; feature extraction | ||
| References | ||
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