Research on mine flood identification and trend prediction method based on video image
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摘要: 分析了矿井水灾视频图像特征，提出了基于视频图像的矿井水灾识别及趋势预测方法，包括水灾视频动态识别、区域分割、面积估算及趋势预测，并通过了试验验证，得出如下主要结论： ① 阈值像素灰度统计法和像素灰度值统计法均可监测和识别水灾，阈值像素灰度统计法不但可抑制低于灰度阈值的噪声，提高识别的准确性，还可减少像素灰度统计数，增强特定像素灰度范围的对比度。② 阈值分割法和视频差分分割法均可分割水灾区域图像，前者整体性较好，后者细节刻画更强。③ 根据分割出的水灾区域图像可估算突水区域面积及进行趋势预测。Abstract: The characteristics of mine flood video images were analyzed. The mine flood identification and trend prediction methods based on video images were proposed, including flood video dynamic identification, region segmentation, area estimation and trend prediction. The results were verified by experiments. The main conclusions are as follows: ① Both threshold pixel grayscale statistical method and pixel grayscale statistical method can monitor and identify floods. The threshold pixel grayscale statistical method not only can suppresses noise below the grayscale threshold and improve the accuracy of recognition, but also can reduce the pixel grayscale statistics, enhance contrast of a particular pixel grayscale range. ② Both the threshold segmentation method and the video differential segmentation method can segment the image of the flood area, the former is better overall and the latter is more detailed.③ The area of the water inrush area can be estimated and the trend can be forecast based on the segmented flood area image.
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Keywords:
- mine flood /
- image recognition /
- flood area segmentation /
- area estimation /
- trend predictio
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期刊类型引用(5)
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