Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images

By Qixing Zhang, Gaohua Lin, Yongming Zhang, Gao Xu, Jinjun Wang. 2018

311 citations recorded by the source corpus.

Abstract In this paper, Faster R-CNN was used to detect wildland forest fire smoke to avoid the complex manually feature extraction process in traditional video smoke detection methods. Synthetic smoke images are produced by inserting real smoke or simulative smoke into forest background to solve the lack of training data. The models trained by the two kinds of synthetic images respectively are tested in dataset consisting of real fire smoke images. The results show that simulative smoke is the better choice and the model is insensitive to thin smoke. It may be possible to further boost the performance by improving the synthetic process of forest fire smoke images or extending this solution to video sequences.

Subfields: Computer Vision and Pattern Recognition, Global and Planetary Change, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/aa0c1d9f131d561495b2fc663cd7bd5bcff42b62

Citations in the corpus

• A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing (2020, unknown): “[63] trained a faster R-CNN for forest smoke detection by creating synthetic smoke images and achieved great performance when tested on real smoke images.”