Title: UAV patrol detection of forest fire burning point based on YOLOv3-SPP algorithm

Authors: Lishan Ma; Yuanjie Ding; Shunhu Dong; Wenjun Chen; Shenghong Wang

Addresses: Guoluo Power Supply Company of State Grid Qinghai Electric Power Company, Guoluo, 814000, China ' Guoluo Power Supply Company of State Grid Qinghai Electric Power Company, Guoluo, 814000, China ' Guoluo Power Supply Company of State Grid Qinghai Electric Power Company, Guoluo, 814000, China ' Guoluo Power Supply Company of State Grid Qinghai Electric Power Company, Guoluo, 814000, China ' Guoluo Power Supply Company of State Grid Qinghai Electric Power Company, Guoluo, 814000, China

Abstract: To overcome the problems of low recall rate, poor positioning accuracy and long time of UAV patrol inspection of forest fire location detection method, this paper proposes a UAV patrol inspection method of forest fire burning point location based on YOLOv3-SPP algorithm. Firstly, the forest fire images are collected by UAV patrol technology. Then, the two-dimensional Gaussian function is used for image filtering to realise fire image processing. Finally, YOLOv3 target detection network is constructed, image feature fusion is carried out with SSP method, and the sigmoid detection function of burning point position is constructed and solved to realise the detection of burning point position of forest fire. The results show that the detection recall rate of this method is 98%, the positioning accuracy is 98%, and the detection time is only 9.2 s, indicating that this method can effectively improve the detection effect of fire burning point location.

Keywords: YOLOv3; two-dimensional Gaussian function; SPP; forest fire.

DOI: 10.1504/IJRIS.2024.144059

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.6, pp.480 - 489

Received: 17 Feb 2023
Accepted: 27 Apr 2023
Published online: 23 Jan 2025 *

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