VIT-AP University, Amaravati, Andhra Pradesh 522237, India.
Faculty of Technology, University of Colombo, Colombo, Sri Lanka.
Comput Intell Neurosci. 2022 Jul 9;2022:3170244. doi: 10.1155/2022/3170244. eCollection 2022.
Over the last few decades, forest fires are increased due to deforestation and global warming. Many trees and animals in the forest are affected by forest fires. Technology can be efficiently utilized to solve this problem. Forest fire detection is inevitable for forest fire management. The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective. The mixed learning technique is composed of YOLOv4 tiny and LiDAR techniques. Unmanned aerial vehicles (UAVs) are promising options to patrol the forest by making them fly over the region. The proposed model deployed on an onboard UAV has achieved 1.24 seconds of classification time with an accuracy of 91% and an F1 score of 0.91. The onboard CPU is able to make a 3D model of the forest fire region and can transmit the data in real time to the ground station. The proposed model is trained on both dense and rainforests in detecting and predicting the chances of fire. The proposed model outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.
在过去的几十年中，由于森林砍伐和全球变暖，森林火灾有所增加。森林中的许多树木和动物都受到了森林火灾的影响。可以有效地利用技术来解决这个问题。森林火灾检测对于森林火灾管理是必不可少的。这项工作的目的是提出深度学习技术来预测森林火灾，这将是具有成本效益的。混合学习技术由 YOLOv4 tiny 和 LiDAR 技术组成。无人机 (UAV) 通过让它们在该区域上空飞行，是巡逻森林的有前途的选择。在机载无人机上部署的模型具有 1.24 秒的分类时间，准确率为 91%，F1 得分为 0.91。机载 CPU 能够为森林火灾区域制作 3D 模型，并能够实时将数据传输到地面站。该模型在密集森林和热带雨林中都经过了训练，以检测和预测火灾的可能性。该模型优于传统方法，如贝叶斯分类器、随机森林和支持向量机。
