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A Self-Adaptive Wildfire Detection Algorithm with Two-Dimensional Otsu Optimization

机译:具有二维OTSU优化的自适应野火检测算法

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The gradual increase in wildfires has caused frequent trips and outages along electrical transmission lines, which is a serious threat to the operational stability of power grids. A self-adaptive wildfire detection algorithm has been developed and tested in this paper. Most of existing wildfire detection methods employed fixed thresholds to identify potential wildfire pixels while the background pixels were ignored. By calculating two-dimensional histogram of the brightness temperatures of mid-infrared channel, the threshold selection is self-adaptive and potential pixels containing scenes of fire can be distinguished automatically. Based on the two-dimensional Otsu method and contextual test algorithm, an improved wildfire detection algorithm that uses multitemporal Visible and Infrared Radiometer (VIRR) data is described. The wildfire detection results within three kilometers of electrical transmission lines demonstrate the effectiveness of the proposed method, which has accurate low-temperature wildfire detection ability.
机译:野火的逐步增加导致沿电力传输线路频繁的旅行和中断,这是对电网的操作稳定性的严重威胁。本文开发和测试了自适应野火检测算法。现有的大多数野火检测方法采用固定阈值来识别潜在的野火像素,而忽略背景像素。通过计算中红外频道的亮度温度的二维直方图,阈值选择是自适应的,并且可以自动区分含有火场景的潜在像素。基于二维OTSU方法和上下文测试算法,描述了一种使用多型可见光和红外辐射计(VIRR)数据的改进的野火检测算法。野火检测结果在三公里的电气传输线内证明了该方法的有效性,该方法具有精确的低温野火检测能力。

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