Results 1 to 10 of about 4,791,934 (244)

An Improved YOLOv5s-Seg Detection and Segmentation Model for the Accurate Identification of Forest Fires Based on UAV Infrared Image

Remote Sensing, 2023
With the influence of climate change and human activities, the frequency and scale of forest fires have been increasing continuously, posing a significant threat to the environment and human safety. Therefore, rapid and accurate forest fire detection has
Kunlong Niu, Chongyang Wang, Jianhui Xu, Chuanxun Yang, Xia Zhou, Xiankun Yang   +5 more
doaj   +3 more sources

Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method

Sensors, 2023
Forest fires can destroy forest and inflict great damage to the ecosystem. Fortunately, forest fire detection with video has achieved remarkable results in enabling timely and accurate fire warnings.
Jiarun Huang, Zhili He, Yuwei Guan, Hongguo Zhang   +3 more
doaj   +3 more sources

FuF-Det: An Early Forest Fire Detection Method under Fog

Remote Sensing, 2023
In recent years, frequent forest fires have seriously threatened the earth’s ecosystem and people’s lives and safety. With the development of machine vision and unmanned aerial vehicle (UAVs) technology, UAV monitoring combined with machine vision has ...
Yaxuan Pang, Yiquan Wu, Yubin Yuan
doaj   +2 more sources

Forest Fire Detection and Notification Method Based on AI and IoT Approaches

Future Internet, 2023
There is a high risk of bushfire in spring and autumn, when the air is dry. Do not bring any flammable substances, such as matches or cigarettes. Cooking or wood fires are permitted only in designated areas.
Kuldoshbay Avazov, An Eui Hyun, Alabdulwahab Abrar Sami S, Azizbek Khaitov, Akmalbek Bobomirzaevich Abdusalomov, Young Im Cho   +5 more
doaj   +3 more sources

YOLOGX: an improved forest fire detection algorithm based on YOLOv8

Frontiers in Environmental Science
To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX.
Caixiong Li, Yue Du, Xing Zhang, Peng Wu
doaj   +3 more sources

Wireless sensors and network-based detection of wildfires [PDF]

Big Data and Computing Visions, 2022
We all know forest is very important resource of oxygen. Saving our environmental resources is human beings responsibility. One of the techniques to save forests is forest fire detection.
Aziza Algarni
doaj   +1 more source

Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network

Remote Sensing, 2022
To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued.
Xin Zheng, Feng Chen, Liming Lou, Pengle Cheng, Ying Huang   +4 more
doaj   +1 more source

Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning

Sensors, 2022
The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal ...
Naoto Maeda, Hideyuki Tonooka
doaj   +1 more source

Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer

Remote Sensing, 2023
In the current context of climate change and demographic expansion, one of the phenomena that humanity faces are the suburban wildfires. To prevent the occurrence of suburban forest fires, fire risk assessment and early fire detection approaches need to ...
Panagiotis Barmpoutis, Aristeidis Kastridis, Tania Stathaki, Jing Yuan, Mengjie Shi, Nikos Grammalidis   +5 more
doaj   +1 more source

Automatic wildfire monitoring system based on deep learning

European Journal of Remote Sensing, 2022
Fire detection based on computer vision technology can avoid many flaws in conventional methods. However, existing methods fail to achieve a good trade-off in accuracy, model size, speed, and cost.
Yingshu Peng, Yi Wang
doaj   +1 more source