Open AccessFew-shot Object Detection with Camouflage Animals
Author(s) -
Yi Lei,
Lei Lei
Publication year - 2021
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 - 10.1088/1742-6596/1961/1/012005
Subject(s) - camouflage , computer science , artificial intelligence , shot (pellet) , computer vision , object (grammar) , object detection , feature (linguistics) , set (abstract data type) , annotation , field (mathematics) , quality (philosophy) , training set , pattern recognition (psychology) , mathematics , chemistry , philosophy , organic chemistry , epistemology , pure mathematics , programming language , linguistics
Object detection is a hot issue in the field of computer vision, which is widely used in intelligent video surveillance, medical image analysis, and practice in the fields of military strategy. Previous object detection is based on a lot of training data, and for some special target, it is difficult to find enough annotation data to training model, so few-shot object detection was born. In this paper, a data set of animals with protective colors is constructed(CAD), which contains ten categories of different objects with high quality annotations. We combine the existing attention RPN and Feature Reweighting module to train our net, the experimental results prove that the Camouflage animals in nature can be well identified, and our results obtain 1.9% AP50, 1.2% AP75 improvement compared with before work.