Open AccessActive learning for image preparation of automatic vending machine (AVM) employing transfer learning method
Author(s) -
Fang Li,
Min Zeng,
Jia Xiao,
Xiaojun Li,
Yuanyuan Li,
Guosheng Hu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012114
Subject(s) - annotation , computer science , transfer of learning , active learning (machine learning) , artificial intelligence , automatic image annotation , object (grammar) , machine learning , training set , pattern recognition (psychology) , image retrieval , image (mathematics)
In this paper, we employed active learning methods to prepare annotated images for our training of automatic vending machine (AVM) system, in order to minimize human annotation cost. Due to the tiny data of our system, transfer learning approach is used by implementing the already trained Yolov3-tiny model for COCO dataset as our training start. Also, we evaluated the effectiveness of 3 annotation strategies: smallest annotation area (SAA), largest annotation area (LAA) and moderate annotation area (MAA), for photos of top views from above. The results show that the idea of employing active learning methods to prepare annotated data is feasible. Also, the annotation strategy of MAA demonstrates the superior performance, for its enough object area and the least background area.