基于深度学习的小目标检测研究进展

doi:10.7527/S1000-6893.2020.24691

Abstract

Abstract: Object detection, one of the most fundamental and challenging tasks in computer vision, has achieved remarkable breakthroughs with the rapid development of deep learning methods. However, most of the current state-of-the-art algorithms are designed for medium or large objects with regular sizes or proportions. The performance of detecting small objects is still far from satisfactory due to the small size and weak features of target objects. In recent years, Small Object Traching (SOT), which is widely applied in outdoor remote shooting and aerospace remote sensing scenarios, has drawn significant attention and substantial approaches have emerged. However, few comprehensive reviews on this issue have been conducted. This paper summarizes the research progress of deep learning-based SOT methods in terms of problem definition, algorithm analysis, application introduction, and future prospects. We first present the definition of the SOT problem and illustrate its technical difficulties and challenges faced in practical applications. The main reasons for the low accuracy of small object detectors and the corresponding improvement methods from eight different perspectives are then analyzed, and the SOT research work in various technical aspects summarized in detail. The representative applications of SOT algorithms in several specific scenarios are introduced. Finally, we prospect the development trends and promising directions for future research. Hopefully, this survey can provide reference for the research work in this field.

Key words: object detection, deep learning, small object traching, computer vision, convolutional neural networks

CLC Number:

LI Hongguang, YU Ruonan, DING Wenrui. Research development of small object traching based on deep learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(7): 24691-024691.

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Research development of small object traching based on deep learning

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