Improving the Performance of Object Detection by Preserving Balanced Class Distribution

Heewon Lee and Sangtae Ahn ()
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Heewon Lee: School of Electronics Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Sangtae Ahn: School of Electronics Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

Mathematics, 2023, vol. 11, issue 21, 1-14

Abstract: Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class, is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.

Keywords: computer vision; object detection; imbalanced class distribution; multi-label stratification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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