Pixel-level automatic annotation for forest fire image. (September 2021)

• Record Type: Journal Article
• Authors: Yang, Xubing; Chen, Run; Zhang, Fuquan; Zhang, Li; Fan, Xijian; Ye, Qiaolin; Fu, Liyong
• Is Part Of: Engineering applications of artificial intelligence. Volume 104(2021)
• Publication Date: 2021-09
• Subjects: Fire detection -- Convex hull -- Pixel-level -- Image annotation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104353
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: We propose an automatic annotation method for forest fire images in the level of pixel, where supervise information is introduced by interactive convex hulls. Instead of usual rectangle-/ regular -shaped regions, we propose a convex hull algorithm for visually selecting polygonal ( irregular ) fire and no-fire regions. Guided by the goals of forest fire monitoring systems: high fire detection rate (true-positive) and then low false alarm rate (false-positive), we construct a k-nearest neighbor (kNN) based KD-tree to speed annotation. Compared to state-of-the-art, the proposed method not only widens the view of fire detection from conventional two-class to multi-class classification problem to meet complex forest image background, but also relaxes the limit of i.i.d (independent and identical distribution) hypothesis on machine learning methods. Furthermore, it is simple to use, which just relies on pixel information and avoids considering additional auxiliary features from multiple color spaces. Experimental evaluations are carrying on forest fire images, MIVIA dead-directional videos, and more challenging omni-directional videos. The comparison demonstrates that the proposed pixel-level annotation method is able to achieve higher fire detection rate and lower false alarm rate at the same time. Highlights: To select training samples easily and accurately, we proposed a fast convex hull method. To relax the i.i.d hypothesis, a K-d tree KNN classifier is adopted for pixel annotation. To overcome classic two-class, our method is oriented from multi-class classification. Experimentally it can achieve both high fire detection rate and low false alarm rate.