A survey on vision-based outdoor smoke detection techniques for environmental safety

Early stage smoke detection using image and video analysis is an important area of research due to its enormous applications in mitigating fire hazards and ensuring environmental safety. Numerous solutions have been proposed for real-time smoke detection using conventional image processing, machine learning, and deep learning techniques. Smoke pattern, motion analysis, color and texture are important characteristics that help identify it in the outdoor environment. Vision-based Smoke detection algorithms can be broadly classified into three categories: smoke classification, segmentation, and bounding box estimation. This paper presents a comprehensive survey of existing techniques on smoke detection in the outdoor environment using image and video analysis. To perform the survey, initially 271 articles were collected from different sources like Google Scholar, Science Direct, IEEE Xplore, SpringerLink, Wiley and ACM Digital Library using the keyword search. Based on their focus on the vision-based solutions for the outdoor environment, 126 articles were identified as relevant to the present survey. Starting from the initial IP approaches that are frequently referred in the literature, machine learning and deep learning approaches have also been reviewed for each type of smoke detection. Performance of algorithms, datasets used in the research, evaluation metrics, challenges and future directions of research are also discussed.