Pattern RecognitionNo Access

Small Object Detection Methods in Complex Background: An Overview

College of Artificial Intelligence, North China University of Science and Technology, Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, Hebei, P. R. China

E-mail Address: lizhigang@ncst.edu.cn

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Qimei Guo

https://orcid.org/0000-0003-3746-6990

College of Artificial Intelligence, North China University of Science and Technology, Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, Hebei, P. R. China

E-mail Address: guoqm@stu.ncst.edu.cn

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Bo Sun

College of Artificial Intelligence, North China University of Science and Technology, Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, Hebei, P. R. China

E-mail Address: sunbo@ncst.edu.cn

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Difei Cao

College of Artificial Intelligence, North China University of Science and Technology, Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, Hebei, P. R. China

E-mail Address: caodifei@stu.ncst.edu.cn

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Yingqi Li

College of Artificial Intelligence, North China University of Science and Technology, Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, Hebei, P. R. China

E-mail Address: liyingqi@ncst.edu.cn

Corresponding authors.

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, and

Xiaochuan Sun

College of Artificial Intelligence, North China University of Science and Technology, Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, Hebei, P. R. China

E-mail Address: sunxiaochuan@ncst.edu.cn

Corresponding authors.

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https://doi.org/10.1142/S0218001423500027Cited by:4 (Source: Crossref)

Abstract

Small object detection has been a research hotspot in the field of computer vision. Especially in complex backgrounds (CBs), SOD faces various challenges, including inconspicuous small object features, object distortion due to CBs interference, and inaccurate object localization due to various noises. So far, many methods have been proposed to improve the SOD content in CBs. In this paper, based on an extensive study of related literature, we first outline the current challenges and some cutting-edge solutions for SOD, and then introduce the complex background interference types present in small object images and the imaging characteristics of different types of images, as well as the characteristics of small objects. Next, the image pre-processing methods are summarized. Based on this, machine learning-based SOD methods and traditional SOD methods are focused on. Finally, the future development direction is given.

Keywords:

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