THERMAL SCIENCE
International Scientific Journal
DRONE IMAGERY FOREST FIRE DETECTION AND CLASSIFICATION USING MODIFIED DEEP LEARNING MODEL
ABSTRACT
With the progression of information technologies, unmanned aerial vehicles (UAV) or drones are more significant in remote monitoring the environment. One main application of UAV technology relevant to nature monitoring is monitoring wild animals. Among several natural disasters, Wildfires are one of the deadliest and cause damage to millions of hectares of forest lands or resources which threatens the lives of animals and people. Drones present novel features and convenience which include rapid deployment, adjustable and wider viewpoints, less human intervention, and high maneuverability. With the effective enforcement of deep learning in many applications, it is used in the domain of forest fire recognition for enhancing the accuracy of forest fire detection through extraction of deep semantic features from images. This article concentrates on the design of the drone imagery forest fire detection and classification using modified deep learning (DIFFDC-MDL) model. The presented DIFFDC-MDL model aims in the detection and classification of forest fire in drone imagery. To accomplish this, the presented DIFFDC-MDL model designs a modified MobileNet-v2 model to generate feature vectors. For forest fire classification, a simple recurrent unit model is applied in this study. In order to further improve the classification outcomes, shuffled frog leap algorithm is used. The simulation outcome analysis of the DIFFDC-MDL system was tested utilizing a database comprising fire and non-fire samples. The extensive comparison study referred that the improvements of the DIFFDC-MDL system over other recent algorithms.
KEYWORDS
PAPER SUBMITTED: 2022-10-04
PAPER REVISED: 2022-11-17
PAPER ACCEPTED: 2022-11-22
PUBLISHED ONLINE: 2023-01-21
- Zhan, Jialei, et al. A High-Precision Forest Fire Smoke Detection Approach Based on ARGNet, Computers and Electronics in Agriculture, 196 (2022), 106874
- Sharma, A., Pradeep, K. S., UAV‐Based Framework for Effective Data Analysis of Forest Fire Detection Using 5G Networks: An Effective Approach Towards Smart Cities Solutions, International Journal of Communication Systems, On-line first, onlinelibrary.wiley.com/doi/10.1002/dac.4826, 2021, e4826
- Li, M., et al.,. Residential Electricity Classification Method Based on Cloud Computing Platform and Random Forest, Computer Systems Science and Engineering, 38 (2021), 1, pp. 39-46
- Nguyen, A. Q., et al., A Visual Real-Time Fire Detection Using Single Shot Multibox Detector for Uav-Based Fire Surveillance, Proceedings, 8th International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam, 2020, pp. 338-343
- Ghali, R., et al., Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation, Sensors, 22 (2022), 5, 1977
- Barmpoutis, P., et al., Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures, Remote Sensing, 12 (2020), 19, 3177
- Allauddin, M. S., et al., Development of a Surveillance System for Forest Fire Detection and Monitoring Using Drones, Proceedings, International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 9361-9363
- Li, S., et al., An Early Forest Fire Detection System Based on DJI M300 Drone and H20T Camera, Proceedings, International Conference on Unmanned Aircraft Systems, Dubrovnik, Croatia, 2022, pp. 932-937
- Treneska, S., Stojkoska, B. R., Wildfire Detection from UAV Collected Images Using Transfer Learning, Proceedings, 18th International Conference on Informatics and Information Technologies, Skopje, North Macedonia, 2021, pp. 6-7
- Hossain, F. A., et al., Forest Fire Flame and Smoke Detection From UAV-Captured Images Using Fire-Specific Color Features and Multi-Color Space Local Binary Pattern, Journal of Unmanned Vehicle Systems, 8 (2020), 4, pp. 285-309
- Jiao, Z., et al., A Deep Learning Based Forest Fire Detection Approach Using UAV and YOLOv3, Proceedings,1st International conference on industrial artificial intelligence, Shenyang, China, pp. 1-5
- Fouda, M. M., et al., A Lightweight Hierarchical AI Model for UAV-Enabled Edge Computing With Forest-fire Detection Use-Case, IEEE Network, 2022
- Jiao, Z., et al., A YOLOv3-Based Learning Strategy for Real-Time UAV-Based Forest Fire Detection, Proceedings, Chinese Control And Decision Conference, Hefei, China, pp. 4963-4967
- Rahman, A. Z. R., et al., Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network, Intelligent Automation and Soft Computing, 35 (2023), 3, pp. 3259-3277
- Hossain, F. A., et al., Wildfire Flame and Smoke Detection Using Static Image Features and Artificial Neural Network, Proceedings, 1st International Conference on Industrial Artificial Intelligence, Shenyang, China, pp. 1-6
- Zhang, L., et al., A Forest Fire Recognition Method Using UAV Images Based on Transfer Learning, Forests, 13 (2022), 7, 975
- Chen, Y., et al., UAV Image-Based Forest Fire Detection Approach Using Convolutional Neural Network, Proceedings, 14th IEEE Conference on Industrial Electronics and Applications, Xi'an, China, pp. 2118-2123
- Maqsood, S., et al., Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM, Medicina, 58 (2022), 8, 1090
- Gumaei, A., et al., A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data, IEEE Access, 7 (2019), Aug., pp.99152-99160
- Bai, S., et al., Spatial and Temporal Characteristics of Rainfall Anomalies in 1961-2010 in the Yangtze River Basin, China, Atmosphere, 8 (2022), 02112, 960
- Liu, Y., et al.,. Simulated Annealing-Based Dynamic Step Shuffled Frog Leaping Algorithm: Optimal Performance Design and Feature Selection, Neurocomputing, 503 (2022), Sept., pp. 325-362
- Shamsoshoara, A., et al., Aerial Imagery Pile Burn Detection Using Deep Learning: The FLAME Dataset, Computer Networks, 193 (2021), 108001
