论文标题
图像处理失败和草坪测量中的深度学习成功
Image Processing Failure and Deep Learning Success in Lawn Measurement
论文作者
论文摘要
草坪区域的测量是图像处理和深度学习的应用。研究人员已被使用分层网络，分段图像和许多其他方法来测量草坪区域。方法有效性和准确性各不相同。在此项目中，已经比较了该项目图像处理和深度学习方法，以找到测量草坪区域的最佳方法。将使用OPENCV的三种图像处理方法与卷积神经网络进行了比较，卷积神经网络是最著名，最有效的深度学习方法之一。我们使用Keras和Tensorflow估计草坪区域。为此，卷积神经网络或不久的是CNN表现出很高的精度（94-97％）。在图像处理方法中，具有80-87％精度和边缘检测的阈值是测量草坪区域的有效方法，但精度为26-31％的轮廓并不能成功计算出草坪区域。我们可以得出结论，与图像处理和其他深度学习技术相比，深度学习方法特别是CNN可能是最佳侦探方法。
Lawn area measurement is an application of image processing and deep learning. Researchers have been used hierarchical networks, segmented images and many other methods to measure lawn area. Methods effectiveness and accuracy varies. In this project Image processing and deep learning methods has been compared to find the best way to measure the lawn area. Three Image processing methods using OpenCV has been compared to Convolutional Neural network which is one of the most famous and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional Neural Network or shortly CNN shows very high accuracy (94-97%) for this purpose. In image processing methods, Thresholding with 80-87% accuracy and Edge detection are effective methods to measure the lawn area but Contouring with 26-31% accuracy does not calculate the lawn area successfully. We may conclude that deep learning methods especially CNN could be the best detective method comparing to image processing and other deep learning techniques.
