Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation
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Fecha
2019
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Institute of Electrical and Electronics Engineers Inc.
Nombre de Curso
Licencia CC
Atribución 4.0 Internacional (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device. In order to quantify the number of berries per image, a network based on Mask R-CNN for object detection and instance segmentation was proposed. Several backbones such as ResNet101, ResNet50 and MobileNetV1 were tested. The performance of the algorithm was evaluated using the Intersection over Union Error (IoU) and the competitive mean Average Precision (mAP) per images and per batch. The best detection result was obtained with the ResNet50 backbone achieving a mIoU score of 0.595 and mAP scores of 0.759 and 0.724 respectively (for IoU thresholds 0.5 and 0.7). For instance segmentation, the best results obtained were 0.726 for the mIoU metric and 0.909 and 0.774 for the mAP metric using thresholds of 0.5 and 0.7 respectively. © 2013 IEEE.
Notas
Indexación: Scopus
Palabras clave
Blueberries, deep learning, quantification, segmentation
Citación
IEEE Access Volume 7, Pages 105776 - 1057882019 Article number 8787818
DOI
10.1109/ACCESS.2019.2933062