Deep Encoder–Decoder Network-Based Wildfire Segmentation Using Drone Images in Real-Time

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 24; p. 6302
• Main Authors: Muksimova, Shakhnoza, Mardieva, Sevara, Cho, Young-Im
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Accuracy; cameras; Coders; Data collection; Datasets; Deep learning; drone; Drones; Economic impact; encoder–decoder; Environmental degradation; False alarms; Fire alarms; fire detection; Fire fighting; Forest & brush fires; forest fire and smoke segmentation; Forest fire detection; Forest fires; forests; humans; Image processing; Image segmentation; Methods; Neural networks; Real time; Remote sensing; Remote sensing systems; Robustness; Semantics; Sensors; Smoke; Unmanned aerial vehicles; Wildfires; United States > US
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14246302

Wildfire is a hazardous natural phenomenon that leads to significant human fatalities, catastrophic environmental damages, and economic losses. Over the past few years, the intensity and frequency of fires have increased worldwide. Studies have been conducted to develop distinctive solutions to minimize forest fires. Systems for distant fire detection and monitoring have been established, showing improvements in data collection and fire characterization. However, wildfires cover vast areas, making other proposed ground systems unsuitable for optimal coverage. Unmanned aerial vehicles (UAVs) have become the subject of active research in recent years. Deep learning-based image-processing methods demonstrate improved performance in various tasks, including detection and segmentation, which can be utilized to develop modern forest firefighting techniques. In this study, we established a novel two-pathway encoder–decoder-based model to detect and accurately segment wildfires and smoke from the images captured using UAVs in real-time. Our proposed nested decoder uses pre-activated residual blocks and an attention-gating mechanism, thereby improving segmentation accuracy. Moreover, to facilitate robust and generalized training, we prepared a new dataset comprising actual incidences of forest fires and smoke, varying from small to large areas. In terms of practicality, the experimental results reveal that our method significantly outperforms existing detection and segmentation methods, despite being lightweight. In addition, the proposed model is reliable and robust for detecting and segmenting drone camera images from different viewpoints in the presence of wildfire and smoke.