{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:52:43Z","timestamp":1780444363059,"version":"3.54.1"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>Fire detection from images or videos has gained a growing interest in recent years due to the criticality of the application. Both reliable real-time detectors and efficient retrieval techniques, able to process large databases acquired by sensor networks, are needed. Even if the reliability of artificial vision methods improved in the last years, some issues are still open problems. In particular, literature methods often reveal a low generalization capability when employed in scenarios different from the training ones in terms of framing distance, surrounding environment, or weather conditions. This can be addressed by considering contextual information and, more specifically, using vision-language models capable of interpreting and describing the framed scene. In this work, we propose FIRE-TASTIC: Fire Recognition with Task-Aware Spatio-Temporal Image Captioning, a novel framework to use object detectors in conjunction with vision-language models for fire detection and information retrieval. The localization capability of the former makes it able to detect even tiny fire traces but expose the system to false alarms. These are strongly reduced by the impressive zero-shot generalization capability of the latter, which can recognize and describe fire-like objects without prior fine-tuning. We also present a variant of the FIRE-TASTIC framework based on visual question answering instead of image captioning, which allows one to customize the retrieved information with personalized questions. To integrate the high-level information provided by both neural networks, we propose a novel method to query the vision-language models using the temporal and spatial localization information provided by the object detector. The proposal can improve the retrieval performance, as evidenced by the experiments conducted on two recent fire detection datasets, showing the effectiveness and the generalization capabilities of FIRE-TASTIC, which surpasses the state of the art. Moreover, the vision-language model, which is unsuitable for video processing due to its high computational load, is executed only on suspicious frames, allowing for real-time processing. This makes FIRE-TASTIC suitable for both real-time processing and information retrieval on large datasets.<\/jats:p>","DOI":"10.1145\/3721291","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T09:10:27Z","timestamp":1740993027000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Video Fire Recognition Using Zero-Shot Vision-Language Models Guided by a Task-Aware Object Detector"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-8160","authenticated-orcid":false,"given":"Diego","family":"Gragnaniello","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5495-2432","authenticated-orcid":false,"given":"Antonio","family":"Greco","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8176-6950","authenticated-orcid":false,"given":"Carlo","family":"Sansone","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7687-4929","authenticated-orcid":false,"given":"Bruno","family":"Vento","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"issue":"3","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.3390\/s23031512","article-title":"An improved forest fire detection method based on the Detectron2 model and a deep learning approach","volume":"23","author":"Abdusalomov Akmalbek Bobomirzaevich","year":"2023","unstructured":"Akmalbek Bobomirzaevich Abdusalomov, Bappy Md Siful Islam, Rashid Nasimov, Mukhriddin Mukhiddinov, and Taeg Keun Whangbo. 2023. An improved forest fire detection method based on the Detectron2 model and a deep learning approach. Sensors 23, 3 (2023), 1512.","journal-title":"Sensors"},{"key":"e_1_3_1_3_2","first-page":"1","volume-title":"Proceedings of the 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","author":"Nasser Alkalouti Hanan","year":"2021","unstructured":"Hanan Nasser Alkalouti, Mayada Ahmed, and A. L. Masre. 2021. Encoder-decoder model for automatic video captioning using YOLO algorithm. In Proceedings of the 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE, 1\u20134."},{"issue":"8","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.3390\/s22082929","article-title":"A robust fire detection model via convolution neural networks for intelligent robot vision sensing","volume":"22","author":"An Qing","year":"2022","unstructured":"Qing An, Xijiang Chen, Junqian Zhang, Ruizhe Shi, Yuanjun Yang, and Wei Huang. 2022. A robust fire detection model via convolution neural networks for intelligent robot vision sensing. Sensors 22, 8 (2022), 2929.","journal-title":"Sensors"},{"key":"e_1_3_1_5_2","first-page":"1","volume-title":"Proceedings of the International Joint Conference on Neural Networks (IJCNN)","author":"Ayala Angel","year":"2020","unstructured":"Angel Ayala, Bruno Fernandes, Francisco Cruz, David Mac\u00eado, Adriano L. I. Oliveira, and Cleber Zanchettin. 2020. KutralNet: A portable deep learning model for fire recognition. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1\u20138."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3151660"},{"key":"e_1_3_1_7_2","first-page":"1820","volume-title":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"32","author":"Cao Yichao","year":"2021","unstructured":"Yichao Cao, Qingfei Tang, Xuehui Wu, and Xiaobo Lu. 2021. EFFNet: Enhanced feature foreground network for video smoke source prediction and detection. IEEE Transactions on Circuits and Systems for Video Technology 32, 4 (2021), 1820\u20131833."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2008.05.005"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2006.12.003"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2013.07.003"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982994"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.09.026"},{"key":"e_1_3_1_13_2","first-page":"1707","volume-title":"Proceedings of the International Conference on Image Processing (ICIP)","volume":"3","author":"Chen Thou-Ho","year":"2004","unstructured":"Thou-Ho Chen, Ping-Hsueh Wu, and Yung-Chuen Chiou. 2004. An early fire-detection method based on image processing. In Proceedings of the International Conference on Image Processing (ICIP), Vol. 3, 1707\u20131710."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-03882-9"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08260-2"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07467-z"},{"key":"e_1_3_1_17_2","first-page":"1","volume-title":"Proceedings of the IEEE Latin American Conference on Computational Intelligence (LA-CCI)","author":"Ven\u00e2ncio Pedro Vin\u00edcius A. B. De","year":"2021","unstructured":"Pedro Vin\u00edcius A. B. De Ven\u00e2ncio, Tamires M. Rezende, Adriano C. Lisboa, and Adriano V. Barbosa. 2021. Fire detection based on a two-dimensional convolutional neural network and temporal analysis. In Proceedings of the IEEE Latin American Conference on Computational Intelligence (LA-CCI), 1\u20136."},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1109\/WACV.2018.00201","volume-title":"Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Desta Mikyas T.","year":"2018","unstructured":"Mikyas T. Desta, Larry Chen, and Tomasz Kornuta. 2018. Object-based reasoning in VQA. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1814\u20131823."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14041007"},{"key":"e_1_3_1_20_2","first-page":"13548","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Dorkenwald Michael","year":"2024","unstructured":"Michael Dorkenwald, Nimrod Barazani, Cees G. M. Snoek, and Yuki M. Asano. 2024. PIN: Positional insert unlocks object localisation abilities in VLMs. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 13548\u201313558."},{"key":"e_1_3_1_21_2","first-page":"1261","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Dumitriu Andrei","year":"2023","unstructured":"Andrei Dumitriu, Florin Tatui, Florin Miron, Radu Tudor Ionescu, and Radu Timofte. 2023. Rip current segmentation: A novel benchmark and YOLOv8 baseline results. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 1261\u20131271."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01855"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2015.2392531"},{"issue":"1","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jnlssr.2021.01.001","article-title":"Understanding future changes to fires in Southern Europe and their impacts on the wildland-urban interface","volume":"2","author":"Ganteaume Anne","year":"2021","unstructured":"Anne Ganteaume, Renaud Barbero, Marielle Jappiot, and Eric Maill\u00e9. 2021. Understanding future changes to fires in Southern Europe and their impacts on the wildland-urban interface. Journal of Safety Science and Resilience 2, 1 (2021), 20\u201329.","journal-title":"Journal of Safety Science and Resilience"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10694-020-01064-z"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124783"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s12652-024-04939-z","article-title":"Onfire 2023 contest: What did we learn about real time fire detection from cameras","author":"Gragnaniello Diego","year":"2025","unstructured":"Diego Gragnaniello, Antonio Greco, Carlo Sansone, and Bruno Vento. 2025. Onfire 2023 contest: What did we learn about real time fire detection from cameras? Journal of Ambient Intelligence and Humanized Computing 16 (2025), 253\u2013264.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109543"},{"key":"e_1_3_1_29_2","first-page":"1","volume-title":"Proceedings of the IEEE International Conference on Cognitive Machine Intelligence (CogMI)","author":"Hogan Isaac","year":"2021","unstructured":"Isaac Hogan, Donghao Qiao, Ruikang Luo, Mojtaba Moattari, Austin Carthy, Farhana Zulkernine, Fran\u00e7ois Rivest, and M\u00e9lanie Breton. 2021. FireWarn: Fire hazards detection using deep learning models. In Proceedings of the IEEE International Conference on Cognitive Machine Intelligence (CogMI), 1\u201310."},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNSC.2005.1461169"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3295748"},{"issue":"17","key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"14813","DOI":"10.1609\/aaai.v35i17.17739","article-title":"Project RISE: Recognizing industrial smoke emissions","volume":"35","author":"Hsu Yen-Chia","year":"2021","unstructured":"Yen-Chia Hsu, Ting-Hao Kenneth Huang, Ting-Yao Hu, Paul Dille, Sean Prendi, Ryan Hoffman, Anastasia Tsuhlares, Jessica Pachuta, Randy Sargent, and Illah Nourbakhsh. 2021. Project RISE: Recognizing industrial smoke emissions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, No. 17, 14813\u201314821.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108219"},{"issue":"4","key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.3390\/s23041894","article-title":"Real-time forest fire detection by ensemble lightweight YOLOX-L and defogging method","volume":"23","author":"Huang Jiarun","year":"2023","unstructured":"Jiarun Huang, Zhili He, Yuwei Guan, and Hongguo Zhang. 2023. Real-time forest fire detection by ensemble lightweight YOLOX-L and defogging method. Sensors 23, 4 (2023), 1894.","journal-title":"Sensors"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1007\/s10694-021-01199-7","article-title":"A deep separable convolutional neural network for multiscale image-based smoke detection","volume":"58","author":"Huo Yinuo","year":"2022","unstructured":"Yinuo Huo, Qixing Zhang, Yang Jia, Dongcai Liu, Jinfu Guan, Gaohua Lin, and Yongming Zhang. 2022. A deep separable convolutional neural network for multiscale image-based smoke detection. Fire Technology 58 (2022), 1445\u20131468.","journal-title":"Fire Technology"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2022.103690"},{"key":"e_1_3_1_37_2","unstructured":"Arpit Jadon Mohd Omama Akshay Varshney Mohammad Samar Ansari and Rishabh Sharma. 2019. FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications. arXiv:1905.11922. Retrieved from https:\/\/arxiv.org\/abs\/1905.11922"},{"key":"e_1_3_1_38_2","unstructured":"Glenn Jocher Chaurasia Ayush and Jing Qiu. 2023. Ultralytics YOLO. Retrieved from https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9142862"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3473037"},{"issue":"12","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1109\/TCSVT.2011.2157190","article-title":"Modeling and formalization of fuzzy finite automata for detection of irregular fire flames","volume":"21","author":"Ko Byoung Chul","year":"2011","unstructured":"Byoung Chul Ko, Sun Jae Ham, and Jae Yeal Nam. 2011. Modeling and formalization of fuzzy finite automata for detection of irregular fire flames. IEEE Transactions on Circuits and Systems for Video Technology 21, 12 (2011), 1903\u20131912. Retrieved from https:\/\/cvpr.kmu.ac.kr\/","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13053148"},{"key":"e_1_3_1_43_2","first-page":"19730","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Li Junnan","year":"2023","unstructured":"Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In Proceedings of the International Conference on Machine Learning. PMLR, 19730\u201319742."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.019"},{"issue":"5","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1007\/s10694-019-00832-w","article-title":"Smoke detection on video sequences using 3D convolutional neural networks","volume":"55","author":"Lin Gaohua","year":"2019","unstructured":"Gaohua Lin, Yongming Zhang, Gao Xu, and Qixing Zhang. 2019. Smoke detection on video sequences using 3D convolutional neural networks. Fire Technology 55, 5 (2019), 1827\u20131847.","journal-title":"Fire Technology"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_1_47_2","first-page":"134","volume-title":"Proceedings of the 17th International Conference on Pattern Recognition (ICPR \u201904), Vol","volume":"4","author":"Liu Che-Bin","year":"2004","unstructured":"Che-Bin Liu and Narendra Ahuja. 2004. Vision based fire detection. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR \u201904), Vol. 4. IEEE, 134\u2013137."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-5090-2"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3538641.3561491"},{"issue":"4","key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s10462-012-9345-z","article-title":"Automatic fire detection based on soft computing techniques: Review from 2000 to 2010","volume":"42","author":"Mahdipour Elham","year":"2014","unstructured":"Elham Mahdipour and Chitra Dadkhah. 2014. Automatic fire detection based on soft computing techniques: Review from 2000 to 2010. Artificial Intelligence Review 42, 4 (2014), 895\u2013934. (2014)","journal-title":"Artificial Intelligence Review"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09832-7"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2006.02.001"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.04.083"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.culher.2020.04.003","article-title":"Assessing and mitigating vulnerability and fire risk in historic centres: A cost-benefit analysis","volume":"45","author":"Tozo Neto Julio","year":"2020","unstructured":"Julio Tozo Neto and Tiago Miguel Ferreira. 2020. Assessing and mitigating vulnerability and fire risk in historic centres: A cost-benefit analysis. Journal of Cultural Heritage 45 (2020), 279\u2013290.","journal-title":"Journal of Cultural Heritage"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3122346"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20059-5_10"},{"key":"e_1_3_1_57_2","first-page":"256","volume-title":"Proceedings of the 2021 19th OITS International Conference on Information Technology (OCIT)","year":"2021","unstructured":"Soumya and Priyadarsini Panda, Navin Chandra. 2021. A visual question answering system using YOLO model. In Proceedings of the 2021 19th OITS International Conference on Information Technology (OCIT). IEEE, 256\u2013260."},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20082202"},{"issue":"6","key":"e_1_3_1_59_2","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1007\/s10694-019-00872-2","article-title":"Dual deep learning model for image based smoke detection","volume":"55","author":"Singh Pundir Arun","year":"2019","unstructured":"Arun Singh Pundir and Balasubramanian Raman. 2019. Dual deep learning model for image based smoke detection. Fire Technology 55, 6 (2019), 2419\u20132442.","journal-title":"Fire Technology"},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1007\/s11042-021-11224-0","article-title":"A fire monitoring and alarm system based on channel-wise pruned YOLOv3","author":"Qian Huimin","year":"2022","unstructured":"Huimin Qian, Fei Shi, Wei Chen, Yilong Ma, and Min Huang. 2022. A fire monitoring and alarm system based on channel-wise pruned YOLOv3. Multimedia Tools and Applications 81 (2022), 1833\u20131851.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-020-01044-0"},{"issue":"1","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s11069-020-04197-0","article-title":"Wildfire impacts on schools and hospitals following the 2018 California camp fire","volume":"104","author":"Schulze Stefanie S.","year":"2020","unstructured":"Stefanie S. Schulze, Erica C. Fischer, Sara Hamideh, and Hussam Mahmoud. 2020. Wildfire impacts on schools and hospitals following the 2018 California camp fire. Natural Hazards 104, 1 (2020), 901\u2013925.","journal-title":"Natural Hazards"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11276-2"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08809-1"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119371"},{"issue":"19","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"18749","DOI":"10.1109\/JIOT.2022.3162016","article-title":"CENet: A channel-enhanced spatiotemporal network with sufficient supervision information for recognizing industrial smoke emissions","volume":"9","author":"Tao Huanjie","year":"2022","unstructured":"Huanjie Tao, Chao Xie, Jing Wang, and Zhouxin Xin. 2022. CENet: A channel-enhanced spatiotemporal network with sufficient supervision information for recognizing industrial smoke emissions. IEEE Internet of Things Journal 9, 19 (2022), 18749\u201318759.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.3390\/make5040083"},{"key":"e_1_3_1_69_2","first-page":"136","volume-title":"Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)","author":"Thomson William","year":"2020","unstructured":"William Thomson, Neelanjan Bhowmik, and Toby P. Breckon. 2020. Efficient and compact convolutional neural network architectures for non-temporal real-time fire detection. In Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 136\u2013141."},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.06.015"},{"key":"e_1_3_1_71_2","first-page":"294","volume-title":"Proceedings of the International Conference on Image Analysis and Processing","author":"Vincent Grace","year":"2023","unstructured":"Grace Vincent, Laura Desantis, Ethan Patten, and Sambit Bhattacharya. 2023. Rapid fire detection with early exiting. In Proceedings of the International Conference on Image Analysis and Processing. Springer, 294\u2013301."},{"issue":"4","key":"e_1_3_1_72_2","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1007\/s10694-022-01260-z","article-title":"Real-time video fire detection via modified YOLOv5 network model","volume":"58","author":"Wu Zongsheng","year":"2022","unstructured":"Zongsheng Wu, Ru Xue, and Hong Li. 2022. Real-time video fire detection via modified YOLOv5 network model. Fire Technology 58, 4 (2022), 2377\u20132403.","journal-title":"Fire Technology"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123081"},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11042-017-5561-5","article-title":"Recurrent convolutional network for video-based smoke detection","volume":"78","author":"Engxia Yin M.","year":"2019","unstructured":"M. Engxia Yin, Congyan Lang, Zun Li, Songhe Feng, and Tao Wang. 2019. Recurrent convolutional network for video-based smoke detection. Multimedia Tools and Applications 78 (2019), 237\u2013256.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2747399"},{"key":"e_1_3_1_76_2","first-page":"282","volume-title":"Proceedings of the International Conference on Image Analysis and Processing","author":"Zedda Luca","year":"2023","unstructured":"Luca Zedda, Andrea Loddo, and Cecilia Di Ruberto. 2023. FIRESTART: Fire ignition recognition with enhanced smoothing techniques and Real-Time tracking. In Proceedings of the International Conference on Image Analysis and Processing. Springer, 282\u2013293."},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3369699"},{"issue":"8","key":"e_1_3_1_78_2","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1007\/s11760-018-1319-4","article-title":"A convolutional neural network-based flame detection method in video sequence","volume":"12","author":"Zhong Zhen","year":"2018","unstructured":"Zhen Zhong, Minjuan Wang, Yukun Shi, and Wanlin Gao. 2018. A convolutional neural network-based flame detection method in video sequence. Signal, Image and Video Processing 12, 8 (2018), 1619\u20131627.","journal-title":"Signal, Image and Video Processing"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3721291","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T21:24:31Z","timestamp":1760477071000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":77,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10,31]]}},"alternative-id":["10.1145\/3721291"],"URL":"https:\/\/doi.org\/10.1145\/3721291","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"2024-06-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}