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Fs-yolo: fire-smoke detection based on improved YOLOv7

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Abstract

Fire has emerged as a major danger to the Earth’s ecological equilibrium and human well-being. Fire detection and alert systems are essential. There is a scarcity of public fire datasets with examples of fire and smoke in real-world situations. Moreover, techniques for recognizing items in fire smoke are imprecise and unreliable when it comes to identifying small objects. We developed a dual dataset to evaluate the model’s ability to handle these difficulties. Introducing FS-YOLO, a new fire detection model with improved accuracy. Training YOLOv7 may lead to overfitting because of the large number of parameters and the limited fire detection object categories. YOLOv7 struggles to recognize small dense objects during feature extraction, resulting in missed detections. The Swin Transformer module has been enhanced to decrease local feature interdependence, obtain a wider range of parameters, and handle features at several levels. The improvements strengthen the model’s robustness and the network’s ability to recognize dense tiny objects. The efficient channel attention was incorporated to reduce the occurrence of false fire detections. Localizing the region of interest and extracting meaningful information aids the model in identifying pertinent areas and minimizing false detections. The proposal also considers using fire-smoke and real-fire-smoke datasets. The latter dataset simulates real-world conditions with occlusions, lens blur, and motion blur. This dataset tests the model’s robustness and adaptability in complex situations. On both datasets, the mAP of FS-YOLO is improved by 6.4\(\%\) and 5.4\(\%\) compared to YOLOv7. In the robustness check experiments, the mAP of FS-YOLO is 4.1\(\%\) and 3.1\(\%\) higher than that of today’s SOTA models YOLOv8s, DINO.

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Data availability

Our dataset and code will be made public upon acceptance of the paper.

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Acknowledgements

I would like to acknowledge Professor Wang, who set an example of excellence as a researcher, for her kind support and wise advice.

Funding

The National Natural Science Foundation of China(6210 3096),The Natural Science Foundation of Hainan Province (623MS071),The "Chunhui Plan" cooperative scientific research project of the Ministry of Education(HZKY202 20314),Hainan Province Science and Technology Special Fund (ZDYF2022SHFZ105), The Natural Science Foundation of Heilongjiang Province (LH2022F009), The Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City (2021JJLH0025),Guiding Innovation Foundation of Northeast Petroleum University (15071202 203).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Northeast Petroleum University, Daging, 163318, China

    Dongmei Wang, Ying Qian, Jingyi Lu, Peng Wang, Zhongrui Hu & Yongkang Chai

  2. SANYA Offshore 0il & Gas Research Institute, Northeast Petroleum University, Sanya, 572023, China

    Dongmei Wang, Ying Qian, Jingyi Lu & Peng Wang

  3. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing, 163318, China

    Jingyi Lu & Peng Wang

  4. Artificial Intelligence Energy Research lnstitute, Northeast Petroleum University, Daqing, 163318, China

    Jingyi Lu & Peng Wang

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  1. Dongmei Wang
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  4. Peng Wang
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Correspondence to Jingyi Lu.

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Communicated by Q. Xu.

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Wang, D., Qian, Y., Lu, J. et al. Fs-yolo: fire-smoke detection based on improved YOLOv7. Multimedia Systems 30, 215 (2024). https://doi.org/10.1007/s00530-024-01359-z

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