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Adaptive Flame Detection Using Video Image Processing and Optimization Algorithm | ||
| Al-Zahraa Journal for Artificial Intelligence Applications | ||
| Article 1, Volume 0, Issue 1, September 2026, Pages 1-13 PDF (911.6 K) | ||
| Document Type: Original Article | ||
| DOI: 10.66723/zjaia.2026.163376.1001 | ||
| Author | ||
| Zahraa Shihab Hakeem* | ||
| Al-zahraa University for Women | ||
| Abstract | ||
| Fires in farms, wheat fields, and homes have increased as a result of rising temperatures. Conventional smoke sensors are ineffective in open environments. Video Image Detection (VID) provides a compelling approach for detecting flames. This paper presents a fire detection system that employs a video camera able of adapting to environmental changes in color characteristics and illuminance. The proposed flame detection system merges two methods to achieve accurate detection. While the second method uses motion detection for flames, the first method concentrates on color identification. Using a one-level Lifting Wavelet Transform (LWT) to the Red Green Blue (RGB) input video reduces the processed data by 75% while preserving the essential fire features. Then, the frames are classified and extracted from the video with the help of some feature detectors. Using the firing threshold criterion, the area of fire is calculated and boxed accordingly. The proposed system for colour detection has two types of thresholds, which are adaptive thresholds of Kapur and Otsu criteria and Forest Optimization Algorithm (FOA) based objective functions. Based on the experimental results, the system is quite accurate. In real-time situations, the adaptive threshold flame detection system manages to achieve 97.5% indoors and 92.4% outdoors. The system works on time, i.e., within 0.6s. The system is performing robustly in different environmental situations and can identify fire inside buildings as well as outside. | ||
| Keywords | ||
| Fire Detection; Video Image Detection; Forest Optimization Algorithm | ||
| References | ||
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