Case Studies in Thermal Engineering· 2020Q1
Image fire detection algorithms based on convolutional neural networks
- 373citations
- Q1SCImago
- 2020year
Short summary
YOLO v3-based image fire detection achieves 83.7% average precision, outperforming other CNN models and traditional methods.
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Key points
- YOLO v3 achieved 83.7% average precision in image fire detection.
- The YOLO v3 model offers a detection speed of 28 FPS, suitable for real-time applications.
- CNN-based object detection models outperform traditional feature extraction methods in accuracy and speed.
- YOLO v3 demonstrated superior robustness compared to other tested CNN models.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Case Studies in Thermal Engineering, 2020 · DOI ↗
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Field: Safety, Risk, Reliability and Quality
Safety, Risk, Reliability and QualityEngineering