探地雷达树木根系定位与直径估算

针对树木根系的探地雷达(Ground Penetrating Radar,GPR)检测图像复杂、解译困难、自动化程度低且精度不高等问题,该研究提出了一种基于YOLOv3的树根自动识别和参数估计的方法。通过不同的根预埋试验分析了根直径、掩埋深度和朝向对根系识别和预测的影响,对比评估了在复杂现场环境下该方法与商业软件常用的阈值分割方法的识别效果。结果表明算法可实现根系自动提取和双曲线顶点定位,对根双曲线的识别准确率和召回率分别达到了96.62%和86.94%,根系参数预测的总平均相对误差在10.57%以内。该方法具有较高的识别准确率和鲁棒性,可实时地对树木根系进行检测并进行根系参数预测,对树木根系无损检测具有重要意义。

Estimating the location and diameter of tree roots using ground penetrating radar

Abstract: Non-destructive testing of tree roots can contribute to explore the interaction between plant roots and soil. Currently, ground penetrating radar (GPR) can make it possible to non-destructively measure tree roots. However, the complex images from GPR are difficult to parse, due to the low automation level and accuracy. In this study, an automatic estimation of root and diameter was proposed using YOLOv3 to realize the intelligent recognition and extraction of root reflection hyperbola region of interest (ROI). Image processing such as histogram equalization was applied to improve the difference between the hyperbola and background, thereby to optimize the dataset of edge detection. Random Hough transform (RHT) with strong anti-clutter interference was used to realize the precise positioning of the apex of the hyperbola. A Savizky-Golay filter was introduced to smooth the discrete sequence of GPR one-dimensional data (A-scan), thereby to improve the prediction accuracy of root diameter. A diameter estimation was addressed for the distribution of two positions of hyperbola vertex in A-scan data. A root embedding field test was conducted to evaluate the effectiveness of models, further to determine the influence of root diameter, buried depth, and orientation in three dimensions on the parameter prediction of root system. The average relative error of root position and diameter was within 25.82% in all experiments. All embedded roots were accurately identified in the test of root diameter, where the average relative errors of diameter, X- and Y-position were 16.04%, 2.22%, and 4.81%, respectively. The embedded roots at different depths were also accurately identified in the test of root buried depth. The apex of the hyperbola was still accurately located in the test, although there was a little interference from clutter. The vertical positioning was corrected through multiple sets of GPR two-dimensional data (B-scan), when the root was tilted along the vertical direction in the root orientation test. The recognition precision and recall rate reached 96.62% and 86.94%, respectively. The average detection time was 40ms for a single image. A better performance was achieved to identify the thick roots in the field test, compared with the threshold segmentation. In addition, two influencing factors were determined to improve the data accuracy, including the soil and root moisture content. Three influencing factors were determined for the predicted accuracy, including the training degree of network model, the image processing, and the RHT threshold. Finally, the improvement strategy was given in the future research. The experimental and analytical results demonstrated that the proposed method can automatically extract the ROI of root reflection hyperbola, further to locate accurately the hyperbola vertex, and finally to replace manual calibration and interpretation. The finding can make a great contribution to the practical exploration for the root location and diameter prediction in the non-destructive testing of tree roots.