Open Access

REVIEW

Confusing Object Detection: A Survey

by Kunkun Tong^1,#, Guchu Zou^2,#, Xin Tan^1,*, Jingyu Gong¹, Zhenyi Qi², Zhizhong Zhang¹, Yuan Xie¹, Lizhuang Ma¹

1 School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
2 Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, 200050, China

* Corresponding Author: Xin Tan. Email:
# These authors contributed equally to this work

Computers, Materials & Continua 2024, 80(3), 3421-3461. https://doi.org/10.32604/cmc.2024.055327

Received 23 June 2024; Accepted 07 August 2024; Issue published 12 September 2024

Abstract

Confusing object detection (COD), such as glass, mirrors, and camouflaged objects, represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds, leveraging deep learning methodologies. Despite garnering increasing attention in computer vision, the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures. As of now, there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks. To fill this gap, our study presents a pioneering review that covers both the models and the publicly available benchmark datasets, while also identifying potential directions for future research in this field. The current dataset primarily focuses on single confusing object detection at the image level, with some studies extending to video-level data. We conduct an in-depth analysis of deep learning architectures, revealing that the current state-of-the-art (SOTA) COD methods demonstrate promising performance in single object detection. We also compile and provide detailed descriptions of widely used datasets relevant to these detection tasks. Our endeavor extends to discussing the limitations observed in current methodologies, alongside proposed solutions aimed at enhancing detection accuracy. Additionally, we deliberate on relevant applications and outline future research trajectories, aiming to catalyze advancements in the field of glass, mirror, and camouflaged object detection.

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Keywords

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