Mask R-CNN for quality control of table olives

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 obta...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 82; no. 14; pp. 21657 - 21671
Main Authors Macías-Macías, Miguel, Sánchez-Santamaria, Héctor, García Orellana, Carlos J., González-Velasco, Horacio M., Gallardo-Caballero, Ramón, García-Manso, Antonio
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2023
Springer Nature B.V
Subjects
Artificial neural networks
Automation
Classification
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Discriminant analysis
Fruits
Machine learning
Multimedia
Multimedia Information Systems
Neural networks
Olive oil
Olives
Quality control
Special Purpose and Application-Based Systems
Mask-RCNN
Table olives
Deep-learning
Object detection
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Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-14668-8