Smoke detection algorithm based on negative sample mining
Joint Authors
Ma, Pei
Yu, Feng
Zhou, Changlong
Jiang, Minghua
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 4 (31 Jul. 2022), pp.695-703, 9 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-07-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Forest fire is one of the most dangerous disasters that threaten the safety of human life and property.
In order to detect fire in time, we detect the smoke when the fire breaks out.
However, it is still a challenging task due to the variations of smoke in color, texture, shape and the disturbances of smoke-like objects.
Therefore, the accuracy of smoke detection is not high, and it is accompanied by a high false positive rate, especially in the real environment.
To tackle this problem, this paper proposes a novel model based on Faster Region-based Convolutional Network (R-CNN) which utilizes negative sample mining method.
The proposed method allows the model to learn more negative sample features, thereby reducing false positives in smoke detection.
The experiments are performed on self-created dataset containing 11958 images which are collected from cameras placed in villages or towns and existing datasets.
Compared to other smoke datasets, the self-created dataset is larger and contains complex scenes.
The proposed method achieves 94.59% accuracy, 94.35% precision and 5.76% false positive rate on self-created dataset.
The results show that the proposed network is better and more robust than previous works.
American Psychological Association (APA)
Ma, Pei& Yu, Feng& Zhou, Changlong& Jiang, Minghua. 2022. Smoke detection algorithm based on negative sample mining. The International Arab Journal of Information Technology،Vol. 19, no. 4, pp.695-703.
https://search.emarefa.net/detail/BIM-1437343
Modern Language Association (MLA)
Ma, Pei…[et al.]. Smoke detection algorithm based on negative sample mining. The International Arab Journal of Information Technology Vol. 19, no. 4 (Jul. 2022), pp.695-703.
https://search.emarefa.net/detail/BIM-1437343
American Medical Association (AMA)
Ma, Pei& Yu, Feng& Zhou, Changlong& Jiang, Minghua. Smoke detection algorithm based on negative sample mining. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 4, pp.695-703.
https://search.emarefa.net/detail/BIM-1437343
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 701-703
Record ID
BIM-1437343