Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging
Abstract
:1. Introduction
2. Related Literature
3. Methods
3.1. Representation of Orientation of a Rigid Body
3.2. Image Capturing
3.3. Convolutional Neural Network Architecture
3.4. Configuration
3.5. Training and Evaluation
4. Results
5. Discussion
Author Contributions
Funding
Conflicts of Interest
References
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Deep-6DPose [%] | InceptionV3 [%] | ResNet50 [%] | VGG19 [%] | |
---|---|---|---|---|
Lie algebra | 98.36 | 91.92 | 95.21 | 88.49 |
Quaternion | 97.12 | 82.74 | 94.11 | 89.22 |
Acc20deg [%] | Acc15deg [%] | Acc10deg [%] | Acc5deg [%] | |
---|---|---|---|---|
Lie algebra | 99.18 | 98.36 | 95.92 | 64.94 |
Quaternion | 99.32 | 97.12 | 86.68 | 46.87 |
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Giefer, L.A.; Arango Castellanos, J.D.; Babr, M.M.; Freitag, M. Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging. Processes 2019, 7, 424. https://doi.org/10.3390/pr7070424
Giefer LA, Arango Castellanos JD, Babr MM, Freitag M. Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging. Processes. 2019; 7(7):424. https://doi.org/10.3390/pr7070424
Chicago/Turabian StyleGiefer, Lino Antoni, Juan Daniel Arango Castellanos, Mohammad Mohammadzadeh Babr, and Michael Freitag. 2019. "Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging" Processes 7, no. 7: 424. https://doi.org/10.3390/pr7070424