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Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection
- Source :
- Sensors, Vol 19, Iss 9, p 2130 (2019), Sensors (Basel, Switzerland), Sensors, Volume 19, Issue 9
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- This paper presents an automatic parameter tuning procedure specially developed for a dynamic adaptive thresholding algorithm for fruit detection. One of the major algorithm strengths is its high detection performances using a small set of training images. The algorithm enables robust detection in highly-variable lighting conditions. The image is dynamically split into variably-sized regions, where each region has approximately homogeneous lighting conditions. Nine thresholds were selected to accommodate three different illumination levels for three different dimensions in four color spaces: RGB, HSI, LAB, and NDI. Each color space uses a different method to represent a pixel in an image: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), LAB (Lightness, Green to Red and Blue to Yellow) and NDI (Normalized Difference Index, which represents the normal difference between the RGB color dimensions). The thresholds were selected by quantifying the required relation between the true positive rate and false positive rate. A tuning process was developed to determine the best fit values of the algorithm parameters to enable easy adaption to different kinds of fruits (shapes, colors) and environments (illumination conditions). Extensive analyses were conducted on three different databases acquired in natural growing conditions: red apples (nine images with 113 apples), green grape clusters (129 images with 1078 grape clusters), and yellow peppers (30 images with 73 peppers). These databases are provided as part of this paper for future developments. The algorithm was evaluated using cross-validation with 70% images for training and 30% images for testing. The algorithm successfully detected apples and peppers in variable lighting conditions resulting with an F-score of 93.17% and 99.31% respectively. Results show the importance of the tuning process for the generalization of the algorithm to different kinds of fruits and environments. In addition, this research revealed the importance of evaluating different color spaces since for each kind of fruit, a different color space might be superior over the others. The LAB color space is most robust to noise. The algorithm is robust to changes in the threshold learned by the training process and to noise effects in images. This research was partially supported by the European Commission (SWEEPER GA No. 664313) and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the Marcus Endowment Fund, and the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering.
- Subjects :
- Lightness
adaptive thresholding
Computer science
fruit detection
Color
Signalbehandling
Color space
lcsh:Chemical technology
01 natural sciences
Biochemistry
Article
Analytical Chemistry
Automation
Image Processing, Computer-Assisted
Vitis
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
Hue
Pixel
business.industry
010401 analytical chemistry
Pattern recognition
parameter tuning
04 agricultural and veterinary sciences
Thresholding
Atomic and Molecular Physics, and Optics
0104 chemical sciences
Databases as Topic
ROC Curve
Fruit
Malus
Lab color space
Signal Processing
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
RGB color model
Noise (video)
Artificial intelligence
Capsicum
business
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 19
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....aaecc16ffbb818a8538d788b0abd812f