论文标题
通过机器学习开发从无人机基于无人机的航空图像的自动计数软件
Development of Automatic Tree Counting Software from UAV Based Aerial Images With Machine Learning
论文作者
论文摘要
无人驾驶汽车（UAV）在许多应用领域成功使用，例如军事，安全，监测，紧急援助，旅游，农业和林业。这项研究旨在从无人机获得的高分辨率图像中自动计算锡尔特大学校园指定区域的树木。使用Adobe Photoshop的Photo Merge工具将30米高的图像在30米高的高度上获得20％的重叠。通过分别施加3x3中位数和平均滤波器，将所得的图像授予并平滑。在某些区域生成了无人机捕获的空中图像的正射图之后，这些地图上不同物体的边界框以HSV（色调饱和值），RGB（红色绿色蓝色）和灰色的方式标记。生成培训，验证和测试数据集，然后使用各种机器学习算法评估了与树检测有关的分类成功率。在最后一步中，通过获得实际树数来建立一个基础真实模型，然后通过将参考地面真实数据与所提出的模型进行比较来计算预测性能。据认为，使用MLP分类器在预定区域中获得的平均准确率为87％，已经取得了重大成功。
Unmanned aerial vehicles (UAV) are used successfully in many application areas such as military, security, monitoring, emergency aid, tourism, agriculture, and forestry. This study aims to automatically count trees in designated areas on the Siirt University campus from high-resolution images obtained by UAV. Images obtained at 30 meters height with 20% overlap were stitched offline at the ground station using Adobe Photoshop's photo merge tool. The resulting image was denoised and smoothed by applying the 3x3 median and mean filter, respectively. After generating the orthophoto map of the aerial images captured by the UAV in certain regions, the bounding boxes of different objects on these maps were labeled in the modalities of HSV (Hue Saturation Value), RGB (Red Green Blue) and Gray. Training, validation, and test datasets were generated and then have been evaluated for classification success rates related to tree detection using various machine learning algorithms. In the last step, a ground truth model was established by obtaining the actual tree numbers, and then the prediction performance was calculated by comparing the reference ground truth data with the proposed model. It is considered that significant success has been achieved for tree count with an average accuracy rate of 87% obtained using the MLP classifier in predetermined regions.
