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Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

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postprint-Multi-modal deep learning Fuji apple detetion using RGB-D cameras and their radiometric capabilites.pdf (1,417Mb)
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10.1016/j.compag.2019.05.016
 
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Gené Mola, Jordi
Vilaplana Besler, VerónicaMés informacióMés informacióMés informació
Rosell Polo, Joan Ramon
Morros Rubió, Josep RamonMés informacióMés informacióMés informació
Ruiz Hidalgo, JavierMés informacióMés informacióMés informació
Gregorio, Eduard
Document typeArticle
Defense date2019-07-01
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time-of-flight (ToF) and, besides color and depth, provide the backscatter signal intensity. However, this radiometric capability has not been exploited for fruit detection applications. This work presents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of the database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. To do so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectance of the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted for use with five-channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of 4.46% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8% when all channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuable information for fruit detection.
CitationGené, J. [et al.]. Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities. "Computers and electronics in agriculture", 1 Juliol 2019, vol. 162, p. 689-698. 
URIhttp://hdl.handle.net/2117/175186
DOI10.1016/j.compag.2019.05.016
ISSN0168-1699
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0168169919301413
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  • Departament de Teoria del Senyal i Comunicacions - Articles de revista [2.351]
  • GPI - Grup de Processament d'Imatge i Vídeo - Articles de revista [117]
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