{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T04:37:22Z","timestamp":1782016642854,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied.<\/jats:p>","DOI":"10.3390\/s24154786","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T10:46:20Z","timestamp":1721817980000},"page":"4786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An Improved Fire and Smoke Detection Method Based on YOLOv8n for Smart Factories"],"prefix":"10.3390","volume":"24","author":[{"given":"Ziyang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingye","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiong Lee Kong","family":"Robert","sequence":"additional","affiliation":[{"name":"School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Baijnath-Rodino, J.A., Kumar, M., Rivera, M., Tran, K.D., and Banerjee, T. (2021). How vulnerable are American states to wildfires? A livelihood vulnerability assessment. Fire, 4.","DOI":"10.3390\/fire4030054"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e4403","DOI":"10.1002\/ecs2.4403","article-title":"Changes in wildfire occurrence and risk to homes from 1990 through 2019 in the Southern Rocky Mountains, USA","volume":"14","author":"Hawbaker","year":"2023","journal-title":"Ecosphere"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Banerjee, T. (2020). Impacts of forest thinning on wildland fire behavior. Forests, 11.","DOI":"10.3390\/f11090918"},{"key":"ref_4","unstructured":"Manpower, M.O. (2023). Workplace Safety and Health Report, Ministry of Manpower Services Centre."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1175\/2008WAF2007062.1","article-title":"Suitability of the Weather Research and Forecasting (WRF) model to predict the June 2005 fire weather for Interior Alaska","volume":"23","year":"2008","journal-title":"Weather Forecast."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kumar, M., Kosovi\u0107, B., Nayak, H.P., Porter, W.C., Randerson, J.T., and Banerjee, T. (2024). Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event. Front. Earth Sci., 11.","DOI":"10.3389\/feart.2023.1305124"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, 20.","DOI":"10.3390\/s20226442"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lu, K., Xu, R., Li, J., Lv, Y., Lin, H., and Liu, Y. (2022). A vision-based detection and spatial localization scheme for forest fire inspection from uav. Forests, 13.","DOI":"10.3390\/f13030383"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, R., Lin, H., Lu, K., Cao, L., and Liu, Y. (2021). A forest fire detection system based on ensemble learning. Forests, 12.","DOI":"10.3390\/f12020217"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wahyono, Harjoko, A., Dharmawan, A., Adhinata, F.D., Kosala, G., and Jo, K.-H. (2022). Real-time forest fire detection framework based on artificial intelligence using color probability model and motion feature analysis. Fire, 5.","DOI":"10.3390\/fire5010023"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s42408-022-00165-0","article-title":"Forest fire and smoke detection using deep learning-based learning without forgetting","volume":"19","author":"Sathishkumar","year":"2023","journal-title":"Fire Ecol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Abdusalomov, A.B., Islam, B.M.S., Nasimov, R., Mukhiddinov, M., and Whangbo, T.K. (2023). An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors, 23.","DOI":"10.3390\/s23031512"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rahul, M., Saketh, K.S., Sanjeet, A., and Naik, N.S. (2020, January 16\u201319). Early detection of forest fire using deep learning. Proceedings of the 2020 IEEE Region 10 Conference (Tencon), Osaka, Japan.","DOI":"10.1109\/TENCON50793.2020.9293722"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Avazov, K., Mukhiddinov, M., Makhmudov, F., and Cho, Y.I. (2021). Fire detection method in smart city environments using a deep-learning-based approach. Electronics, 11.","DOI":"10.3390\/electronics11010073"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"120465","DOI":"10.1016\/j.eswa.2023.120465","article-title":"A modified YOLOv5 architecture for efficient fire detection in smart cities","volume":"231","author":"Yar","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Norkobil Saydirasulovich, S., Abdusalomov, A., Jamil, M.K., Nasimov, R., Kozhamzharova, D., and Cho, Y.-I. (2023). A YOLOv6-based improved fire detection approach for smart city environments. Sensors, 23.","DOI":"10.3390\/s23063161"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20939","DOI":"10.1007\/s00521-023-08809-1","article-title":"An improved fire detection approach based on YOLO-v8 for smart cities","volume":"35","author":"Talaat","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"821","DOI":"10.2298\/CSIS101012030Z","article-title":"SVM based forest fire detection using static and dynamic features","volume":"8","author":"Zhao","year":"2011","journal-title":"Comput. Sci. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TCSVT.2015.2392531","article-title":"Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion","volume":"25","author":"Foggia","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1016\/j.engappai.2012.05.007","article-title":"Fire flame detection in video sequences using multi-stage pattern recognition techniques","volume":"25","author":"Truong","year":"2012","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.firesaf.2010.04.001","article-title":"Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks","volume":"45","author":"Ko","year":"2010","journal-title":"Fire Saf. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1007\/s11760-017-1102-y","article-title":"Video fire detection based on Gaussian Mixture Model and multi-color features","volume":"11","author":"Han","year":"2017","journal-title":"Signal Image Video Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s10694-009-0110-z","article-title":"Video fire smoke detection using motion and color features","volume":"46","author":"Chunyu","year":"2010","journal-title":"Fire Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1061\/(ASCE)CP.1943-5487.0000141","article-title":"Automated color model\u2013based concrete detection in construction-site images by using machine learning algorithms","volume":"26","author":"Son","year":"2012","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_30","first-page":"1","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., and Doll\u00e1r, P. (2017, January 22\u201329). Girshick Ross. Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chopde, A., Magon, A., and Bhatkar, S. (2022, January 10\u201312). Forest Fire Detection and Prediction from image processing using RCNN. Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering, Virtual.","DOI":"10.11159\/iceptp22.204"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Dimitropoulos, K., Kaza, K., and Grammalidis, N. (2019, January 12\u201317). Fire detection from images using faster R-CNN and multidimensional texture analysis. Proceedings of the ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682647"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, M., Ding, Y., and Bu, X. (2023). MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection. Forests, 14.","DOI":"10.3390\/f14030616"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pan, J., Ou, X., and Xu, L. (2021). A collaborative region detection and grading framework for forest fire smoke using weakly supervised fine segmentation and lightweight faster-RCNN. Forests, 12.","DOI":"10.3390\/f12060768"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, H., Jia, Z., and Li, Z. (2023). BL-YOLOv8: An improved road defect detection model based on YOLOv8. Sensors, 23.","DOI":"10.3390\/s23208361"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, G., Wang, J., Nie, Z., Yang, H., and Yu, S. (2023). A lightweight YOLOv8 tomato detection algorithm combining feature enhancement and attention. Agronomy, 13.","DOI":"10.3390\/agronomy13071824"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_40","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_41","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vasu, P.K.A., Gabriel, J., Zhu, J., Tuzel, O., and Ranjan, A. (2023, January 17\u201324). Mobileone: An improved one millisecond mobile backbone. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00764"},{"key":"ref_43","unstructured":"Xia, X., Li, J., Wu, J., Wang, X., Xiao, X., Zheng, M., and Wang, R. (2022). TRT-ViT: TensorRT-oriented vision transformer. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chino, D.Y., Avalhais, L.P., Rodrigues, J.F., and Traina, A.J. (2015, January 26\u201329). Bowfire: Detection of fire in still images by integrating pixel color and texture analysis. Proceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, Brazil.","DOI":"10.1109\/SIBGRAPI.2015.19"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1049\/iet-ipr.2016.0193","article-title":"Real-time multi-feature based fire flame detection in video","volume":"11","author":"Chi","year":"2017","journal-title":"IET Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.firesaf.2015.03.001","article-title":"Dynamic texture based smoke detection using Surfacelet transform and HMT model","volume":"73","author":"Ye","year":"2015","journal-title":"Fire Saf. J."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_50","unstructured":"Ultralytics (2024, June 11). YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_52","unstructured":"(2024, June 11). Ultralytics, YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_53","first-page":"9969","article-title":"GhostNetv2: Enhance cheap operation with long-range attention","volume":"35","author":"Tang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_54","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4786\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:22:10Z","timestamp":1760109730000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":54,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24154786"],"URL":"https:\/\/doi.org\/10.3390\/s24154786","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,24]]}}}