The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A Forest Fire Detection System Based on Ensemble Learning
Open accessAbstract
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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Densely Connected Convolutional Networks
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Microsoft COCO: Common Objects in Context
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Xu, X., Zhang, X., & Zhang, T. (2022) · Remote Sensing · doi:10.3390/rs14041018 · Cited by 221
An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems
Abdusalomov, A., Baratov, N., Kutlimuratov, A., & Whangbo, T. K. (2021) · Sensors · doi:10.3390/s21196519 · Cited by 131
Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN
Bahhar, C., Ksibi, A., Ayadi, M., Jamjoom, M. M., Ullah, Z., Soufiene, B. O., & Sakli, H. (2023) · Electronics · doi:10.3390/electronics12010228 · Cited by 99
Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery
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Metadata
- Authors
- Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y.
- Venue
- Forests
- Publisher
- MDPI AG
- Published
- 2021-02-13
- Volume
- 12
- Issue
- 2
- Pages
- 217
- ISSN
- 1999-4907 (ISSN-L)
- OpenAlex
- W3132971810
- Cited by
- 559
- References
- 38
- FWCI
- 81.72
- Type
- journal article
- Language
- en
- Open access
- gold
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