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In the closed and dark environment of the confined space cabin, the traditional video smoke detection method is difficult to find the fire early because of the limitation of lighting conditions. The advantage of fire detection based on infrared video image is that it does not need lighting conditions and has better performance in dark environment. There is a rapid temperature rise process in the confined space at the beginning of the fire, which is more easily captured by infrared cameras. However, there is little research on infrared frame detection methods in confined space. Therefore, based on the limited space environment of aviation industry, this paper studies the smoke detection problem under the infrared framework, and proposes a high-precision fire and smoke image detection algorithm based on infrared double convolution neural network. By modeling the texture features of neural network and infrared smoke frames, and using video frames as an auxiliary means to increase the number of available training images, the problem of insufficient infrared video data sets is solved. The experimental results show that the detection effect of this method is better than other comparison algorithms in limited space, and the detection false alarm rate is effectively reduced.<\/jats:p>","DOI":"10.1007\/s44227-024-00026-z","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T05:02:49Z","timestamp":1713330169000},"page":"153-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Smoke Detection with Dual Convolutional Networks From Infrared Frames"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8196-823X","authenticated-orcid":false,"given":"Li","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiubao","family":"Sui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanhua","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"26_CR1","unstructured":"Blake D (2000) Aircraft Cargo Compartment Smoke Detector Alarm Incidents on U.S-Registered Aircraft, 1974\u20131999; FAA Report, DOT\/FAA\/AR-TN00\/29; United States Federal Aviation Administration: Washington, DC, USA."},{"key":"26_CR2","first-page":"32","volume":"10752","author":"BU Toreyin","year":"2018","unstructured":"Toreyin BU (2018) Smoke detection in compressed video. 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The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization orentity with any financial interest or non-financial interest in the subject matter ormaterials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Consent for participate was obtained from all participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Consent for publication was obtained from all participants.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}