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An Automatic System for Smoke Detection in Outdoor Areas | ||
| Kerbala Journal for Engineering Sciences (KJES) | ||
| Article 2, Volume 3, Issue 1, 2023, Pages 15-31 PDF (717.26 K) | ||
| Document Type: Research Article | ||
| DOI: 10.63463/kjes1069 | ||
| Authors | ||
| Zahraa Al Hakeem* 1; Haider Ismael2; Hawraa Abbas2 | ||
| 1Electronic and Electrical Departments, College of Engineering, University of Kerbala, Karbala, Iraq | ||
| 2Electrical and Electronics Engineering Department, College of Engineering, University of Kerbala, Karbala, Iraq | ||
| Abstract | ||
| Early detection of fires plays a crucial role in minimizing their impact and preventing them from spreading. Every year, the repetition of fires results in the loss of human life, animal life, and plant life. Fire detection has become increasingly desirable and significant in surveillance systems, where traditional methods of detecting smoke relied on smoke sensors. Therefore, this method is ineffective in open and large buildings, and outdoor areas. As a result, this study suggests using computer vision systems to detect smoke in open spaces by using a static camera. To reduce the data size while preserving important details, the input video is framed and decomposed using the Integer Haar Lifting Wavelet Transform (IHLWT). Then, for smoke color detection, a new method called the multi-threshold International Commission on Illumina (CIE) Lab color space is used, which took into account the smoke colors' change from whitish gray to blackish gray. In addition, the Frame Differences (FD) technique is used to detect motion and thus reduce false alarms. The smoke color detection is combined with frame difference techniques. The small pixels are removed via a morphological operation that represents noise. According to the experimental findings, the approach precision for offline videos is greater than 94.7% for eleven videos, while the average detection reaches 92.8% for online (real time) videos. It also reduces false alarms significantly. According to the trials and comparisons, the suggested smoke detection algorithm performs better than the traditional algorithms in many scenarios. It is also simple, efficient, and low in complexity. | ||
| Keywords | ||
| wavelet transform; CIE Lab color space; frame differences | ||
| References | ||
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