Department of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.
Sensors (Basel). 2022 May 10;22(10):3636. doi: 10.3390/s22103636.
Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.
树木高度是林业研究中的一个重要指标。该指标难以直接测量，且风的干扰也增加了测量难度。因此，树木高度的测量一直是专家和学者努力改进的问题。我们提出了一种基于树木鱼眼图像的树木高度测量方法，以提高树木高度测量的准确性。我们的目的是通过提出改进的轻量级目标检测网络 YOLOX-tiny 来提取鱼眼图像中的树木高度极值点。我们添加了 CBAM 注意力机制、迁移学习和数据增强方法，以提高 YOLOX-tiny 的召回率、F 分数、AP 等指标。本研究提高了 YOLOX-tiny 的检测性能。深度学习的使用可以提高测量效率，同时确保测量的准确性和稳定性。结果表明，树木测量的最高相对误差为 4.06%，平均相对误差为 1.62%。分析表明，该方法在所有阶段的表现都优于以往的研究。
