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Deep Regression versus Detection for Counting in Robotic Phenotyping
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Work in robotic phenotyping requires computer vision methods that estimate the number of fruit or grains in an image. To decide what to use, we compared three methods for counting fruit and grains, each method representative of a class of approaches from the literature. These are two methods based on density estimation and regression (single and multiple column), and one method based on object detection. We found that when the density of objects in an image is low, the approaches are comparable, but as the density increases, counting by regression becomes steadily more accurate than counting by detection. With more than a hundred objects per image, the error in the count predicted by detection-based methods is up to 5 times higher than when using regression-based ones.
- Subjects :
- G740 Computer Vision
Control and Optimization
Computer science
Feature extraction
Biomedical Engineering
02 engineering and technology
010501 environmental sciences
01 natural sciences
Column (database)
Image (mathematics)
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
0105 earth and related environmental sciences
business.industry
Mechanical Engineering
Pattern recognition
Density estimation
Regression
Object detection
Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....b1efed2290ec8e704b5fe8c21cacfc66