Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets
المؤلفون المشاركون
Xu, Xinying
Xie, Gang
Guo, Lei
Gao, Jerry
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-05
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153254
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر