Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets

Abstract

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 generalpurpose 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. e 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.

Development of a deep-learning phenotyping tool for analyzing image-based strawberry phenotypes

“…By labeling and annotating all the available objects in the image (those investigated) and training the AI accordingly, the overall performance and robustness of the system were increased. Low-quality annotation severely affects training models ( Guo et al., 2020 ). According to Mullen et al.…”