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A lightweight forest fire detection algorithm based on YOLOv5s
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Huilin LIU1, Qiong FANG1, Yu JIANG1, Huazhang WEI2, **, Tao WANG3, Shuchuan ZHANG4
China Safety Science Journal | 2025, 35(1) : 75 - 83
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China Safety Science Journal | 2025, 35(1): 75-83
Safety engineering technology
A lightweight forest fire detection algorithm based on YOLOv5s
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Huilin LIU1, Qiong FANG1, Yu JIANG1, Huazhang WEI2, **, Tao WANG3, Shuchuan ZHANG4
Affiliations
  • 1 School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China
  • 2 School of Intelligence and Electrical Engineering, Huainan Vocational Technical College, Huainan Anhui 232001, China
  • 3 Key Laboratory of Unmanned Emergency Equipment and Digital Reconstruction of Disaster Processes in Anhui Province, Chuzhou College, Chuzhou Anhui 239099, China
  • 4 School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China
Published: 2025-01-28 doi: 10.16265/j.cnki.issn1003-3033.2025.01.0127
Outline
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In order to solve the problems of complex structure, large scale and difficulty in balancing detection accuracy and efficiency of the current forest fire detection algorithm based on deep learning, a lightweight forest fire detection algorithm based on YOLOv5s was proposed. Firstly, an optimized background difference technique was used to eliminate the interference of fire-like objects in the background image, thus reducing the time required for image analysis. Secondly, a group blending strategy was designed to optimize the conventional convolution, and an efficient channel attention (ECA) mechanism and depthwise separable convolution were incorporated into the C3 module of feature extraction, which enhanced the ability of image feature extraction and fusion and at the same time effectively reduces the number of model parameters. Then, a dynamic non-monotonic focusing mechanism was used to optimize the WIOU loss function, reducing the harmful gradients generated by low-quality samples. Finally, sufficient experimental comparisons between the proposed algorithm and other algorithms on the constructed forest fire dataset. The results show that the proposed algorithm shows good generalization in various scenarios, and the detection accuracy of the flame target can reach 86.1%, which is 2.7% higher than that of the standard YOLOv5s, and the detection speed is increased by 11.4%, which effectively reduces the fire false alarm rate and enhances the detection performance of the model.

YOLOv5s  /  lightweighting  /  forest fire detection  /  depthwise separable convolution  /  attention  /  wise intersection over union(WIOU)
Huilin LIU, Qiong FANG, Yu JIANG, Huazhang WEI, Tao WANG, Shuchuan ZHANG. A lightweight forest fire detection algorithm based on YOLOv5s[J]. China Safety Science Journal, 2025 , 35 (1) : 75 -83 . DOI: 10.16265/j.cnki.issn1003-3033.2025.01.0127
Year 2025 volume 35 Issue 1
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.01.0127
  • Receive Date:2024-08-20
  • Online Date:2025-07-05
  • Published:2025-01-28
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History
  • Received:2024-08-20
  • Revised:2024-10-25
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Affiliations
    1 School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China
    2 School of Intelligence and Electrical Engineering, Huainan Vocational Technical College, Huainan Anhui 232001, China
    3 Key Laboratory of Unmanned Emergency Equipment and Digital Reconstruction of Disaster Processes in Anhui Province, Chuzhou College, Chuzhou Anhui 239099, China
    4 School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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