10,088 research outputs found
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
In this paper, we investigate estimating emergence and biomass traits from
color images and elevation maps of wheat field plots. We employ a
state-of-the-art deconvolutional network for segmentation and convolutional
architectures, with residual and Inception-like layers, to estimate traits via
high dimensional nonlinear regression. Evaluation was performed on two
different species of wheat, grown in field plots for an experimental plant
breeding study. Our framework achieves satisfactory performance with mean and
standard deviation of absolute difference of 1.05 and 1.40 counts for emergence
and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants
from field images are better than the accuracy reported for the similar, but
arguably less difficult, task of counting leaves from indoor images of rosette
plants. Our results for biomass estimation, even with a very small dataset,
improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository:
https://github.com/p2irc/deepwheat_WACV-2018
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
- …