Classification of potential fire outbreaks: A fuzzy modeling approach based on thermal images. (1st September 2019)

• Record Type: Journal Article
• Authors: Sousa, Maria João; Moutinho, Alexandra; Almeida, Miguel
• Is Part Of: Expert systems with applications. Volume 129(2019)
• Page Start: 216
• Page End: 232
• Publication Date: 2019-09-01
• Subjects: Fire detection -- Campsites -- Thermal imaging sensors -- Feature engineering -- Fuzzy inference; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.03.030
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Feature construction for fire detection based on thermal images. Characterization of the response of thermal imaging sensors in fire scenarios. Data-driven fuzzy modeling with high transparency and interpretability. Low complexity models with low computational load for implementation. Abstract: Fire outbreaks are a serious risk in campsites due to the surroundings and dynamic environment of these areas. Due to climate change, conditions of high ignition propensity are becoming more frequent, leading to an increased need for the development of alternative fire prevention systems that can mitigate the consequences of fire incidents. In this context, this work explores thermal imaging data for early detection of fire outbreaks, aiming for application in a real context. To that end, experimental trials were conducted in laboratory and at the venue of a summer festival under real operation conditions. The datasets acquired are characterized in detail, and a feature engineering process is devised for the analysis of the response of thermal imaging sensors to a fire ignition. To deal with high-dimensional data, the feature construction method follows a statistical color-based approach, that characterizes the dynamic changes in the data using three features. Subsequently, this paper proposes a fuzzy modeling approach based on these variables, which is transparent to interpretation and enables the assessment of patterns modeled. The performance of the classification algorithm for detection of fire outbreaks is evaluated and framed with works in the state-of-the-art for this application.