Image Processing Failure and Deep Learning Success in Lawn Measure

EasyChair Preprint 3831

Abstract

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 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 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.

Keyphrases: Contouring, Convolutional Neural Network, Keras, Lawn Measurement, Regression, TensorFlow, Thresholding, edge detection

@booklet{EasyChair:3831,
  author    = {Jessie Wilkins and Vu Nguyen and Bahareh Rahmani},
  title     = {Image Processing Failure and Deep Learning Success  in Lawn Measure},
  howpublished = {EasyChair Preprint 3831},
  year      = {EasyChair, 2020}}