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Sensors 2018, 18(3), 712;

Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery

AInML Lab, School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
School of Automation, Southeast University, Nanjing 210009, China
Shengyao Intelligence Technology Co. Ltd., Shanghai 201112, China
Author to whom correspondence should be addressed.
Received: 8 December 2017 / Revised: 16 February 2018 / Accepted: 19 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset ‘UAV_Fire’. A 15-layered self-learning DCNN architecture named ‘Fire_Net’ is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, ‘Fire_Net’ guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified. View Full-Text
Keywords: UAV; wildfire; deep learning; saliency detection UAV; wildfire; deep learning; saliency detection

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, Y.; Ma, J.; Li, X.; Zhang, J. Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. Sensors 2018, 18, 712.

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