Abstract:
Considering the available data set is small for training the deep learning models for fire flame detection, we propose two approaches to fasten the learning process and i...Show MoreMetadata
Abstract:
Considering the available data set is small for training the deep learning models for fire flame detection, we propose two approaches to fasten the learning process and improve the detection performance based on transfer learning in this paper. Firstly we propose an approach by using and fine-tuning the existing transfer learning models. The other proposed approach is to use multiple transfer learning models to extract the image features, then fuse these features, and lastly employ the machine learning classifier for flame detection. In the latter proposed approach, ensemble learning method is also investigated to enhance the classifying performance. The experimental results demonstrate that both these two approaches can achieve very high detection accuracy for fire flame image detection.
Published in: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)
Date of Conference: 23-25 November 2018
Date Added to IEEE Xplore: 14 April 2019
ISBN Information: