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An Improved Adaptive Threshold RANSAC Method for Medium Tillage Crop Rows Detection

机译:一种改进的自适应阈值RANSAC方法,用于中耕作业行检测

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In view of crop rows with irregular leaves due to different growth conditions in a single frame image, A line extraction method combining vertical projection and adaptive threshold RANSAC is proposed. Firstly, the backbone intervals are obtained through vertical projection map. Then, fitting parameters of different crop rows are obtained from their respective vertical projections. Finally, the adaptive threshold RANSAC fitting is performed in different crop rows fitting intervals obtained by filtered. Experiments show that the detection rate of single frame image is 96.76%, and the accuracy is 92.18%, which are higher than other algorithms. The average detection fitting time is 243ms, which can meet the real-time detection requirements of agricultural machinery.
机译:鉴于单帧图像中的不同生长条件具有不规则叶片的裁剪行,提出了一种线提取方法,组合垂直投影和自适应阈值Ransac。 首先,通过垂直投影映射获得骨干间隔。 然后,从它们各自的垂直投影获得不同作物行的拟合参数。 最后,以通过滤波获得的不同作物行拟合间隔执行自适应阈值Ransac拟合。 实验表明,单帧图像的检出率为96.76%,精度为92.18%,高于其他算法。 平均检测配合时间为243ms,可以满足农业机械的实时检测要求。

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