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

Yi Zhao1, Jiale Ma2, Xiaohui Li3

  • 1AInML Lab, School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China. z1@chd.edu.cn.

Sensors (Basel, Switzerland)
|March 3, 2018
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Summary

This study introduces Fire_Net, a deep convolutional neural network (DCNN) for wildfire detection using unmanned aerial vehicle (UAV) imagery. Fire_Net accurately classifies wildfire images with 98% accuracy and real-time processing speeds.

Keywords:
UAVdeep learningsaliency detectionwildfire

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Wildfire image classification is challenging due to variations in fire features and backgrounds.
  • Deep Convolutional Neural Networks (DCNNs) require large datasets for effective training.
  • Unmanned Aerial Vehicles (UAVs) provide high-resolution georeferenced imagery for surveillance.

Purpose of the Study:

  • To develop a novel saliency detection algorithm for rapid fire area localization and segmentation in aerial images.
  • To create a standardized wildfire image dataset (UAV_Fire) using data augmentation techniques.
  • To present a 15-layered DCNN architecture, Fire_Net, for automated wildfire feature extraction and classification.

Main Methods:

  • A saliency detection algorithm was employed to avoid feature loss during data augmentation for the UAV_Fire dataset.
  • A 15-layered DCNN, Fire_Net, was designed for self-learning fire feature extraction and classification.
  • Various DCNN architectures and key parameters were evaluated to optimize validation accuracy.

Main Results:

  • The proposed Fire_Net architecture achieved an overall accuracy of 98% in wildfire image classification.
  • Fire_Net demonstrated a processing speed of 41.5 ms per image, enabling real-time wildfire inspection.
  • The system accurately identified all 40 sampled wildfire images from news reports.

Conclusions:

  • The developed saliency detection and Fire_Net classification system offers a highly accurate and efficient solution for real-time wildfire detection from UAV imagery.
  • The UAV_Fire dataset and Fire_Net model provide a valuable resource for advancing research in automated wildfire analysis.
  • The practical utility of Fire_Net is confirmed through successful testing on diverse wildfire news imagery.