Image Processing Failure and Deep Learning Success in Lawn Measurement


J. Wilkins1, M. V. Nguyen1 and B. Rahmani1, 2, 1Fontbonne University, USA, 2Maryville University, USA


Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, image processing and deep learning methods were used to find the best way to measure the lawn area. Three image processing methods using OpenCV 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%). In image processing methods, thresholding with 80-87% accuracy and edge detection are the most effective methods to measure the lawn area while the method ofcontouring 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 learning techniques.


Lawn Measurement, Convolutional Neural Network, Thresholding, Edge Detection, Contouring.

Full Text  Volume 10, Number 20