Azala

Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback

Gorde:
Argitaratua izan da:Journal of Robotics and Mechatronics vol. 37, no. 2 (Apr 2025), p. 270
Egile nagusia: Kiyokawa Takuya
Beste egile batzuk: Shirakura Naoki, Katayama Hiroki, Tomochika Keita, Takamatsu, Jun
Argitaratua:
Fuji Technology Press Co. Ltd.
Gaiak:
Datasets
Images
Annotations
Object recognition
Applications programs
Feedback
Vision systems
Collection
Cameras
Usability
Deep learning
Questionnaires
Workloads
Crowdsourcing
Efficiency
Robotics
Sarrera elektronikoa:Citation/Abstract
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Laburpena:Training deep-learning-based vision systems requires the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this issue, we propose a human-in-the-loop dataset-collection method using a web application. To counterbalance workload and performance by encouraging the collection of multi-view object image datasets enjoyably, thereby amplifying motivation, we propose three types of online visual feedback features to track the progress of the collection status. Our experiments thoroughly investigated the influence of each feature on the collection performance and quality of operation. These results indicate the feasibility of annotation and object detection.
ISSN:0915-3942
1883-8049
DOI:10.20965/jrm.2025.p0270
Baliabidea:Advanced Technologies & Aerospace Database