Camera-based wildfire smoke detection for foggy environments


Tas M., Tas Y., Balki O., AYDIN Z., TAŞDEMİR K.

JOURNAL OF ELECTRONIC IMAGING, vol.31, no.5, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.1117/1.jei.31.5.053033
  • Journal Name: JOURNAL OF ELECTRONIC IMAGING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: deep learning, convolutional neural networks, forest fire detection, image dehazing, smoke detection and segmentation, CONVOLUTIONAL NEURAL-NETWORKS, FIRE DETECTION, IMAGE, SURVEILLANCE
  • Erciyes University Affiliated: Yes

Abstract

Smoke is the first visible sign of forest fires and the most commonly used feature for early forest fire detection using data from cameras. However, one of the natural challenges is the dense fog that appears in forests, which decreases the detection accuracy or triggers false alarms. In this study, we propose a system with a deep neural network-based image preprocessing approach that significantly improves the smoke segmentation and classification performance by dehazing the camera view. Our experimental results provide that the classification models reach 99% F1 score for the correct classification of smoke when the image dehazing method is used before the training process. The smoke localization system achieves 60% average precision when the mask region-based convolutional neural network is used with the ResNet101-FPN backbone. The proposed approach can be utilized for all smoke segmentation frameworks to increase fire detection performance. (c) 2022 SPIE and IS&T