Imbalance Problems in Object Detection: A Review

Oksuz K., Cam B. C., Kalkan S., Akbas E.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol.43, no.10, pp.3388-3415, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 43 Issue: 10
  • Publication Date: 2021
  • Doi Number: 10.1109/tpami.2020.2981890
  • Journal Name: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3388-3415
  • Keywords: Object detection, Taxonomy, Feature extraction, Deep learning, Pipelines, Neural networks, Pattern analysis, Object detection, imbalance, class imbalance, scale imbalance, spatial imbalance, objective imbalance, FACE DETECTION
  • Middle East Technical University Affiliated: Yes

Abstract

In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonomy. Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature. In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before. Moreover, in order to keep our review up to date, we provide an accompanying webpage which catalogs papers addressing imbalance problems, according to our problem-based taxonomy. Researchers can track newer studies on this webpage available at: https://github.com/kemaloksuz/ObjectDetectionImbalance.