ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception
This addresses the need for automated adaptation in industrial object detection, reducing reliance on human annotations, though it appears incremental as it builds on interactive perception methods.
The paper tackles the problem of costly data re-collection and annotation for object detection in industrial settings by introducing ODIP, an object detector that adapts to novel domains automatically through interactive perception, outperforming generic and state-of-the-art few-shot detectors.
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 human-annotated 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.