The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Image fire detection algorithms based on convolutional neural networks
Open accessAbstract
As a new fire detection technology, image fire detection has recently played a crucial role in reducing fire losses by alarming users early through early fire detection. Image fire detection is based on an algorithmic analysis of images. However, there is a lower accuracy, delayed detection, and a large amount of computation in common detection algorithms, including manually and machine automatically extracting image features. Therefore, novel image fire detection algorithms based on the advanced object detection CNN models of Faster-RCNN, R–FCN, SSD, and YOLO v3 are proposed in this paper. A comparison of the proposed and current algorithms reveals that the accuracy of fire detection algorithms based on object detection CNNs is higher than other algorithms. Especially, the average precision of the algorithm based on YOLO v3 reaches to 83.7%, which is higher than the other proposed algorithms. Besides, the YOLO v3 also has stronger robustness of detection performance, and its detection speed reaches 28 FPS, thereby satisfying the requirements of real-time detection.
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Cites
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.351 · Cited by 2,743
Benchmark analysis of representative deep neural network architectures
Bianco, S., Cadene, R., Celona, L., & Napoletano, P. (2018) · IEEE Access · doi:10.1109/access.2018.2877890 · Cited by 838
Muhammad, K., Ahmad, J., & Baik, S. W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.04.083 · Cited by 558
Cited by
An improved fire detection approach based on YOLO-v8 for smart cities
Talaat, F. M., & ZainEldin, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08809-1 · Cited by 642
YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint
Wu, W., Guo, L., Gao, H., You, Z., Liu, Y., & Chen, Z. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06764-3 · Cited by 266
Forest fire and smoke detection using deep learning-based learning without forgetting
Sathishkumar, V. E., Cho, J., Subramanian, M., & Naren, O. S. (2023) · Fire Ecology · doi:10.1186/s42408-022-00165-0 · Cited by 259
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
Wu, H., Hu, Y., Wang, W., Mei, X., & Xian, J. (2022) · Sensors · doi:10.3390/s22197420 · Cited by 63
Metadata
- Authors
- Li, P., & Zhao, W.
- Venue
- Case Studies in Thermal Engineering
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 19
- Pages
- 100625
- ISSN
- 2214-157X (ISSN-L)
- OpenAlex
- W3012420847
- Cited by
- 376
- References
- 4
- FWCI
- 49.06
- Type
- journal article
- Language
- en
- Open access
- gold
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