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Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

Author

Listed:
  • Lei Zhao

    (Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China)

  • Luqian Zhi

    (Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China)

  • Cai Zhao

    (Center of Information Management and Development, Taiyuan University of Technology, Taiyuan 030024, China)

  • Wen Zheng

    (Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China
    Center for Big Data Research in Health, Changzhi Medical College, Changzhi 046000, China)

Abstract

For the detection of small targets, fire-like and smoke-like targets in forest fire images, as well as fire detection under different natural lights, an improved Fire-YOLO deep learning algorithm is proposed. The Fire-YOLO detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters. Furthermore, through the promotion of the feature pyramid, the top-performing prediction box is obtained. Fire-YOLO attains excellent results compared to state-of-the-art object detection networks, notably in the detection of small targets of fire and smoke. Overall, the Fire-YOLO detection model can effectively deal with the inspection of small fire targets, as well as fire-like and smoke-like objects. When the input image size is 416 × 416 resolution, the average detection time is 0.04 s per frame, which can provide real-time forest fire detection. Moreover, the algorithm proposed in this paper can also be applied to small target detection under other complicated situations.

Suggested Citation

  • Lei Zhao & Luqian Zhi & Cai Zhao & Wen Zheng, 2022. "Fire-YOLO: A Small Target Object Detection Method for Fire Inspection," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4930-:d:797625
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