Coal stacking identification method of belt conveyor based on surface reconstructio
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摘要: 针对现有带式输送机堆煤识别方法存在的易发生误触发报警、成本高等问题，提出一种采用红外结构光技术快速重建煤流曲面的带式输送机堆煤识别方法。首先，利用红外结构光技术获取堆煤的深度图；其次，将深度图映射为点云图，用点云数据构建凸四边形网，通过近似德劳内剖分法对凸四边形网进行三角剖分，完成堆煤曲面重建；最后，根据三角形顶点到相机的距离及顶点距离小于阈值的三角形面积占总面积的比例，判定是否出现堆煤事故。近似德劳内剖分法用遍历过程代替插入排序过程，存在小概率不满足德劳内性质的情况，但算法复杂度低，可大幅提升三角剖分速度，从而提高堆煤识别的实时性。实验结果表明：红外结构光技术有效地提高了算法对光照的鲁棒性；近似德劳内剖分法的成功率为99466 1%，同等条件下近似德劳内剖分法和经典德劳内剖分法的曲面重建时间分别为128，13493 ms，近似德劳内剖分法在精度满足应用要求的前提下，极大地提高了运算速度；设定恰当的阈值，得出漏检数和误检数均为0；对大量图像处理时间进行统计，得出每帧处理时间小于20 ms，满足实时性要求。Abstract: The existing coal stacking identification method of belt conveyor has problems of false trigger alarm and high cost. In order to solve the above problems, a coal stacking identification method of belt conveyor is proposed. The method uses infrared structured light technology to quickly reconstruct the coal flow surface of belt conveyor.Firstly, the depth map of the coal stacking is obtained by using infrared structured light technology. Secondly, the depth map is mapped to a point cloud map, and the point cloud data is used to construct a convex quadrilateral network. And the convex quadrilateral network is triangulated by the approximate Delaunay subdivision method to complete the reconstruction of the coal stacking surface.Finally, according to the distance from the triangle vertex to the camera and the proportion of the triangle area whose vertex distance is less than the threshold to the total area, it is determined whether there is a coal stacking accident.The approximate Delaunay subdivision method replaces the insertion sorting process with the traversal process. There is a small probability of not satisfying the Delaunay property, but the algorithm complexity is low. Therefore it can improve the real-time performance of coal stacking identification.The experimental results show that the infrared structured light technology improves the algorithm's robustness to illumination effectively. The success rate of the approximate Delaunay subdivision method is 99.466 1%, and the surface reconstruction time of the approximate Delaunay subdivision method and the classic Delaunay subdivision method under the same conditions is 1.28 ms and 134.93 ms respectively. The approximate Delaunay subdivision method improves the calculation speed greatly when the accuracy meets the application requirements. By setting an appropriate threshold, the number of missed detection and the number of false detection are both 0. The statistics of the processing time of a large number of images show that the processing time of each frame is less than 20 ms, which meets the real-time requirements.
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