Earticle

초록

영어

High-quality annotations are crucial for accurate object detection, but widely used datasets like MS-COCO face issues such as missing objects, duplicate labels, and inaccurate bounding boxes. To overcome these problems, MJ-COCO was created through model-driven refinement, increasing annotations from 860,001 to 1,221,970 instances. This paper presents a comparative analysis of MS-COCO and MJCOCO, with a focus on the accuracy of bounding box measurements. We designed a human-in-the-loop evaluation framework with custom software that enables side-by-side visualization of annotations, allowing evaluators to classify outcomes as improved, worse, or ambiguous. We collectively evaluated 41,754 annotations through a human-in-the-loop verification process involving fifteen human evaluators. The results demonstrate that a total of 25,754 annotations were improved, 2,398 were worsened, and 13,623 were ambiguous, for a total quality score of 89.49%. These findings show that MJ-COCO considerably enhances annotation quality and precision over MS-COCO, making it a more consistent and accurate standard for advancing object detection studies. The dataset and software codes are publicly available on Kaggle: https://www.kaggle.com/datasets/mjcoco2025/mj-coco-2025.

목차

Abstract
I. INTRODUCTION
II. DATASET REFINEMENT AND EVALUATION METHOD
A. Data Preparation.
B. Annotation Quality Assessment.
III. DISCUSSION AND EVALUATION
A. Implementation Details
B. Datasets
C. Discussion
D. Evaluation Criteria
IV. Conclusion
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Adnan Hussain [ Sejong University Seoul 143-747, South Korea ]
• Muhammad Afaq [ Sejong University Seoul 143-747, South Korea ]
• Aizaz Ali Shah [ Sejong University Seoul 143-747, South Korea ]
• Safi Ullah [ Sejong University Seoul 143-747, South Korea ]
• Muhammad Munsif [ Sejong University Seoul 143-747, South Korea ]
• Amjid Ali [ Sejong University Seoul 143-747, South Korea ]
• Maleerat Maliyaem [ King Mongkut’s University of Technology North Bangkok ]
• Sung Wook Baik [ Sejong University Seoul 143-747, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004