Open AccessEasy domain adaptation method for filling the species gap in deep learning-based fruit detection
Author(s) -
Wenli Zhang,
Kaizhen Chen,
Jiaqi Wang,
Yun Shi,
Wei Guo
Publication year - 2021
Publication title -
horticulture research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.947
H-Index - 31
eISSN - 2662-6810
pISSN - 2052-7276
DOI - 10.1038/s41438-021-00553-8
Subject(s) - domain adaptation , artificial intelligence , domain (mathematical analysis) , computer science , deep learning , image (mathematics) , transfer of learning , pattern recognition (psychology) , process (computing) , orange (colour) , adaptation (eye) , labeled data , machine learning , computer vision , biology , mathematics , horticulture , classifier (uml) , mathematical analysis , neuroscience , operating system
Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.