Abstract

An effective smoke detection from visual scenes is crucial to avoid large scale fire around the world. But it is still challenging due to its large variations in color, texture and shapes. To improve smoke detection accuracy, a new approach based on deep convolutional neural networks is proposed which can be trained end to end from raw pixel values to classifier outputs and automatically extract features from images. Experiments show that this method achieves 99.4% detection rates with 0.44% false alarm rates on the large dataset which obviously outperforms existing traditional methods.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call