Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets
Joint Authors
Xu, Xinying
Xie, Gang
Guo, Lei
Gao, Jerry
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-05
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In many supervised computer vision tasks such as object detection, manual annotation crowdsourcing platforms are widely used for acquiring large-scale labeled data.
However, the annotation quality may suffer low quality that can severely affect the training of models.
As a result, the evaluation of the annotations within the dataset is critical, yet it has seldom been addressed in object detection.
In this paper, we present a fine-grained annotation quality assessment (FGAQA) framework for evaluating the quality of object detection datasets.
First, we formulate a generic annotation quality assessment framework based on the core general-purpose data quality dimensions, using the bounding box and the label.
Second, cognition theory in terms of hierarchy and continuity is utilized to refine the basic framework, including the consistency of the bounding box, completeness of the category, hierarchical accuracy of the label, and the consistency of the label.
Comprehensive experiments on the two object detection datasets are used for performance evaluation.
It is found that the ground truth annotations of the Urban Traffic Surveillance dataset have more quality issues than the ones of the PASCAL VOC 2007 detection dataset.
The proposed FGAQA framework performs an effective fine-grained evaluation of the annotations, which is significant for quality assurance of annotations from crowdsourcing platforms and the subsequent model’s training.
American Psychological Association (APA)
Guo, Lei& Xu, Xinying& Xie, Gang& Gao, Jerry. 2020. Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1153254
Modern Language Association (MLA)
Guo, Lei…[et al.]. Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1153254
American Medical Association (AMA)
Guo, Lei& Xu, Xinying& Xie, Gang& Gao, Jerry. Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1153254
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153254