Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3

Abdelaziz Triki, Bassem Bouaziz, Walid Mahdi, Jitendra Gaikwad

Abstract

Automatic measurement of functional trait data from digitized herbarium specimen images is of great interest as traditionally, scientists extract such information manually, which is time-consuming and prone to errors. One challenging task in the automated measurement process of functional traits from specimen images is the existence of other objects such as scale-bar, color pallet, specimen label, envelopes, bar-code and stamp, which are mostly placed at different locations on the herbarium-mounting sheet and require special detection method. To detect automatically all these objects, we train a model based on an improved YOLO V3 full-regression deep neural network architecture, which has gained obvious advantages in both speed and accuracy through capturing deep and high-level features. We made some improvements to adjust YOLO V3 for detecting object from digitized herbarium specimen images. A new scale of feature map is added to the existing scales to improve the detection effect on small targets. At the same time, we adopted the fourth detection layer by a 4* up-sampled layer instead of 2* to get a feature map with higher resolution deeper level. The experimental results indicate that our model performed better with mAP-50 of 93.2% compared to 90.1% mean IoU trained by original YOLO V3 model on the test set.

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Paper Citation


in Harvard Style

Triki A., Bouaziz B., Mahdi W. and Gaikwad J. (2020). Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2, pages 523-529. DOI: 10.5220/0009170005230529


in Bibtex Style

@conference{visapp20,
author={Abdelaziz Triki and Bassem Bouaziz and Walid Mahdi and Jitendra Gaikwad},
title={Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={523-529},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009170005230529},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3
SN - 978-989-758-402-2
AU - Triki A.
AU - Bouaziz B.
AU - Mahdi W.
AU - Gaikwad J.
PY - 2020
SP - 523
EP - 529
DO - 10.5220/0009170005230529