DeepSmoke: Deep learning model for smoke detection and segmentation in outdoor environments. (15th November 2021)

• Record Type: Journal Article
• Authors: Khan, Salman; Muhammad, Khan; Hussain, Tanveer; Ser, Javier Del; Cuzzolin, Fabio; Bhattacharyya, Siddhartha; Akhtar, Zahid; de Albuquerque, Victor Hugo C.
• Is Part Of: Expert systems with applications. Volume 182(2021)
• Publication Date: 2021-11-15
• Subjects: Smoke detection and segmentation -- Semantic segmentation -- Foggy surveillance environment -- Wildfires -- Disaster management; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115125
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Smoke detection and localization in both clear and hazy outdoor environments. Using a lightweight CNN architecture called EfficientNet for smoke detection. Employing DeepLabv3+ semantic segmentation architecture for smoke localization. Pixel-wise annotation of a new benchmark dataset for smoke semantic segmentation. Outperformed existing smoke detection and segmentation methods. Abstract: Fire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings.