Automatic fire and smoke detection method for surveillance systems based on dilated cnns
DC Field | Value | Language |
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dc.contributor.author | Valikhujaev, Y. | - |
dc.contributor.author | Abdusalomov, A. | - |
dc.contributor.author | Im, Cho Y. | - |
dc.date.available | 2020-12-14T00:40:55Z | - |
dc.date.created | 2020-12-02 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2073-4433 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79238 | - |
dc.description.abstract | The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | Atmosphere | - |
dc.title | Automatic fire and smoke detection method for surveillance systems based on dilated cnns | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000593638800001 | - |
dc.identifier.doi | 10.3390/atmos11111241 | - |
dc.identifier.bibliographicCitation | Atmosphere, v.11, no.11 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85096526826 | - |
dc.citation.title | Atmosphere | - |
dc.citation.volume | 11 | - |
dc.citation.number | 11 | - |
dc.contributor.affiliatedAuthor | Valikhujaev, Y. | - |
dc.contributor.affiliatedAuthor | Abdusalomov, A. | - |
dc.contributor.affiliatedAuthor | Im, Cho Y. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Dilated convolution | - |
dc.subject.keywordAuthor | Fire detection | - |
dc.subject.keywordAuthor | Smoke detection | - |
dc.subject.keywordPlus | Cameras | - |
dc.subject.keywordPlus | Complex networks | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Cost effectiveness | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Fires | - |
dc.subject.keywordPlus | Network security | - |
dc.subject.keywordPlus | Smoke detectors | - |
dc.subject.keywordPlus | Statistical tests | - |
dc.subject.keywordPlus | Classification performance | - |
dc.subject.keywordPlus | Detection methods | - |
dc.subject.keywordPlus | Monitoring system | - |
dc.subject.keywordPlus | Number of false alarms | - |
dc.subject.keywordPlus | Optimal performance | - |
dc.subject.keywordPlus | Smoke detection systems | - |
dc.subject.keywordPlus | Surveillance systems | - |
dc.subject.keywordPlus | Training and testing | - |
dc.subject.keywordPlus | Smoke | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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