ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception
Journal
IEEE International Conference on Intelligent Robots and Systems
Pages
4474-4479
Date Issued
2021
Author(s)
Abstract
Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems would lead to costly data re-collection and human annotation processes to re-train models. Existing solutions such as semi-supervised and few-shot methods either rely on numerous human annotations or suffer low performance. In this work, we explore a novel object detector based on interactive perception (ODIP), which can be adapted to novel domains in an automated manner. By interacting with a grasping system, ODIP accumulates visual observations of novel objects, learning to identify previously unseen instances without humanannotated data. Extensive experiments show ODIP outperforms both the generic object detector and state-of-the-art few-shot object detector fine-tuned in traditional manners. A demo video is provided to further illustrate the idea [1]. ? 2021 IEEE.
Subjects
Computer vision
Manufacture
Object recognition
Automatic adaptation
Down-stream
Human annotations
Industrial scenarios
Object detectors
Objects detection
Performance
Semi-supervised
Train model
Visual systems
Object detection
SDGs
Type
conference paper