Using Popular Object Detection Methods for Real Time Forest Fire Detection

Wu, Shixiao; Zhang, Libing

doi:10.1109/iscid.2018.00070

Cited by 102 publications

(38 citation statements)

References 2 publications

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“…Recently, deep learning has outperformed traditional manmade features in many fields, and have been widely used in fire detection. Zhang et al [14] created a forest fire benchmark, and used Faster R-CNN (region-based convolutional neural network) [15], Yolo (you only look once) [16][17][18][19], and SSD (single shot multibox detector) [20] to detect fire. They found that SSD was better regarding efficiency, detection accuracy, and early fire detection ability.…”

Section: Introductionmentioning

confidence: 99%

A Forest Fire Detection System Based on Ensemble Learning

Lin

et al. 2021

Forests

369

125

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Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

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Section: Introductionmentioning

confidence: 99%

A Forest Fire Detection System Based on Ensemble Learning

Lin

et al. 2021

Forests

369

125

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“…We discussed this problem not long ago [19]. At that time we got unsatisfied result, for YOLOv3, we extracted 260 features.…”

Section: Discussionmentioning

confidence: 99%

Using PCAand one‐stage detectors for real‐time forest fire detection

Guo

Yang

2020

J. eng.

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“…Various deep learning methods have been proposed for fire and smoke detection. In [10], Wu et al used popular object detection methods like R-CNN, YOLO, and SSD for realtime forest fire detection. Sharma et al [11] instead propose a CNN-based fire detection based on a pre-trained VGG16 and Resnet50 as baseline architecture.…”

Section: State-of-art Video-based Fire/smoke Detectorsmentioning

confidence: 99%

Real-time video fire/smoke detection based on CNN in antifire surveillance systems

Saponara

Elhanashi

Gagliardi

2020

J Real-Time Image Proc

137

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This work presents a real-time video-based fire and smoke detection using YOLOv2 Convolutional Neural Network (CNN) in antifire surveillance systems. YOLOv2 is designed with light-weight neural network architecture to account the requirements of embedded platforms. The training stage is processed off-line with indoor and outdoor fire and smoke image sets in different indoor and outdoor scenarios. Ground truth labeler app is used to generate the ground truth data from the training set. The trained model was tested and compared to the other state-of-the-art methods. We used a large scale of fire/smoke and negative videos in different environments, both indoor (e.g., a railway carriage, container, bus wagon, or home/office) or outdoor (e.g., storage or parking area). YOLOv2 is a better option compared to the other approaches for real-time fire/smoke detection. This work has been deployed in a low-cost embedded device (Jetson Nano), which is composed of a single, fixed camera per scene, working in the visible spectral range. There are not specific requirements for the video camera. Hence, when the proposed solution is applied for safety on-board vehicles, or in transport infrastructures, or smart cities, the camera installed in closed-circuit television surveillance systems can be reused. The achieved experimental results show that the proposed solution is suitable for creating a smart and real-time video-surveillance system for fire/smoke detection.

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Using Popular Object Detection Methods for Real Time Forest Fire Detection

Cited by 102 publications

References 2 publications

A Forest Fire Detection System Based on Ensemble Learning

A Forest Fire Detection System Based on Ensemble Learning

Using PCAand one‐stage detectors for real‐time forest fire detection

Real-time video fire/smoke detection based on CNN in antifire surveillance systems

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