基于合成图像的Faster R-CNN森林火灾烟雾检测

FASTER R-CNN FOREST FIRE SMOKE DETECTION BASED ON SYNTHETIC IMAGES

本文采用合成图像的FasterR-CNN对森林火灾烟雾进行检测,避免了传统视频烟雾检测方法中复杂的人工特征提取过程.合成烟雾图像是将真实或模拟烟雾插入到森林背景中,解决了训练数据缺乏的问题.将真实合成烟雾和模拟合成烟雾分别训练后的模型放在由真实火焰烟雾图像组成的数据集中测试,测试结果表明,模拟烟雾是更好的选择,模型对薄烟不敏感.通过改进森林火灾烟雾图像的合成过程或者将这个解决方案扩展到视频序列中,可以进一步提高它的性能.

In this paper, Faster R-CNN was used to detect 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.