Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images

Steven Puttemans, Kristof Van Beeck, Toon Goedemé

2018

Abstract

Object detection using a boosted cascade of weak classifiers is a principle that has been used in a variety of applications, ranging from pedestrian detection to fruit counting in orchards, and this with a high average precision. In this work we prove that using both the boosted cascade approach suggest by Viola & Jones and the adapted approach based on integral or aggregate channels by Dollár yield promising results on coconut tree detection in aerial images. However with the rise of robust deep learning architectures for both detection and classification, and the significant drop in hardware costs, we wonder if it is feasible to apply deep learning to solve the task of fast and robust coconut tree detection and classification in aerial imagery. We examine both classification- and detection-based architectures for this task. By doing so we prove that deep learning is indeed a feasible alternative for robust coconut tree detection with a high average precision in aerial imagery, keeping attention to known issues with the selected architectures.

Paper Citation

in Harvard Style

Puttemans S., Van Beeck K. and Goedemé T. (2018). Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 230-241. DOI: 10.5220/0006571902300241

in Bibtex Style

@conference{visapp18,
author={Steven Puttemans and Kristof Van Beeck and Toon Goedemé},
title={Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={230-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006571902300241},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images
SN - 978-989-758-290-5
AU - Puttemans S.
AU - Van Beeck K.
AU - Goedemé T.
PY - 2018
SP - 230
EP - 241
DO - 10.5220/0006571902300241
PB - SciTePress