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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 +4 more
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An Improved Forest Fire and Smoke Detection Model Based on YOLOv5
Forests, 2023 Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different forest fire and smoke features in captured forest fire images make detection difficult.
Yunfei Liu
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A forest fire smoke detection model combining convolutional neural network and vision transformer [PDF]
Frontiers in Forests and Global Change, 2023 Forest fires seriously jeopardize forestry resources and endanger people and property. The efficient identification of forest fire smoke, generated from inadequate combustion during the early stage of forest fires, is important for the rapid detection of
Ying Zheng +5 more
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Fast Forest Fire Smoke Detection Using MVMNet [PDF]
Knowledge-Based Systems, 2022 Forest fires are a huge ecological hazard, and smoke is an early characteristic of forest fires. Smoke is present only in a tiny region in images that are captured in the early stages of smoke occurrence or when the smoke is far from the camera ...
Weiwei Cai, Liujun Li, Guoxiong Zhou
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Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features [PDF]
Journal of Electrical and Computer Engineering, 2015 Smoke detection is a very key part of fire recognition in a forest fire surveillance video since the smoke produced by forest fires is visible much before the flames.
Yaqin Zhao, Zhong Zhou, Mingming Xu
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Adversarial Fusion Network for Forest Fire Smoke Detection [PDF]
Forests, 2022 Recent advances suggest that deep learning has been widely used to detect smoke for early forest fire warnings. Despite its remarkable success, this approach has a number of problems in real life application. Deep neural networks only learn deep and abstract representations, while ignoring shallow and detailed representations.
Tingting Li +3 more
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Remote Sensing, 2023 Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation.
Huanyu Yang, Jun Wang, Jiacun Wang
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Early smoke detection of forest fires based on SVM image segmentation [PDF]
Journal of Forest Science, 2019 A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method
Ding Xiong, Lu Yan
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Lightweight forest smoke and fire detection algorithm based on improved YOLOv5.
PLoS ONE, 2023 Smoke and fire detection technology is a key technology for automatically realizing forest monitoring and forest fire warning. One of the most popular algorithms for object detection tasks is YOLOv5. However, it suffers from some challenges, such as high
Jie Yang +4 more
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Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning
Sensors, 2022 To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms ...
Bingjian Sun, Pengle Cheng, Ying Huang
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