Please use this identifier to cite or link to this item: http://hdl.handle.net/10662/21073
Title: Mask R-CNN for quality control of table olive
Authors: Macías Macías, Miguel
Sánchez Santamaría, Héctor
García Orellana, Carlos Javier
González Velasco, Horacio Manuel
Gallardo Caballero, Ramón
García Manso, Antonio
Keywords: Detección de objetos;Máscara RCNN;Aprendizaje profundo;Aceituna de mesa;Object detection;Mask RCNN;Deep learning;Table olives
Issue Date: 2023
Publisher: Springer
Abstract: In this paper we propose an object detector based on deep learning for scanning samples of table olives. For the construction of the system we have used a Mask R-CNN neural network. This network is able to segment the image providing a mask for each of the olives in the sample from which we can obtain the calibre of the object. In addition, the system is able to measure the degree of ripeness of the olives classifying them as green, semi-ripe and ripe, and identifying those fruits that are defective due to disease or damage caused by the harvesting process. The proposed system achieves success rates of 99.8% in the detection of olive fruits in photograms, 93.5% in the classification of fruit by ripeness and close to 80% in the detection of defects.
Description: Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
URI: http://hdl.handle.net/10662/21073
ISSN: 1380-7501
DOI: 10.1007/s11042-023-14668-8
Appears in Collections:DISIT - Artículos

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