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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.04901 |
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| _version_ | 1866909379190587392 |
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| author | Kiyokawa, Takuya Shirakura, Naoki Katayama, Hiroki Tomochika, Keita Takamatsu, Jun |
| author_facet | Kiyokawa, Takuya Shirakura, Naoki Katayama, Hiroki Tomochika, Keita Takamatsu, Jun |
| contents | Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies have attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this, we propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, 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 impact of each feature on collection performance and quality of operation. The results suggested the feasibility of annotation and object detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_04901 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback Kiyokawa, Takuya Shirakura, Naoki Katayama, Hiroki Tomochika, Keita Takamatsu, Jun Computer Vision and Pattern Recognition Multimedia Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies have attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this, we propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, 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 impact of each feature on collection performance and quality of operation. The results suggested the feasibility of annotation and object detection. |
| title | Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2304.04901 |