We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Explainable Deep Learning Framework for Detecting and Localising Smoke and Fire Incidents: Evaluation of Grad-CAM++ and LIME.
- Authors
Apostolopoulos, Ioannis D.; Athanasoula, Ifigeneia; Tzani, Mpesi; Groumpos, Peter P.
- Abstract
Climate change is expected to increase fire events and activity with multiple impacts on human lives. Large grids of forest and city monitoring devices can assist in incident detection, accelerating human intervention in extinguishing fires before they get out of control. Artificial Intelligence promises to automate the detection of fire-related incidents. This study enrols 53,585 fire/smoke and normal images and benchmarks seventeen state-of-the-art Convolutional Neural Networks for distinguishing between the two classes. The Xception network proves to be superior to the rest of the CNNs, obtaining very high accuracy. Grad-CAM++ and LIME algorithms improve the post hoc explainability of Xception and verify that it is learning features found in the critical locations of the image. Both methods agree on the suggested locations, strengthening the abovementioned outcome.
- Subjects
DEEP learning; CLIMATE change; ARTIFICIAL intelligence; CONVOLUTIONAL neural networks; FIRE detectors
- Publication
Machine Learning & Knowledge Extraction, 2022, Vol 4, Issue 4, p1124
- ISSN
2504-4990
- Publication type
Article
- DOI
10.3390/make4040057