by Haonan Tian
2025,7(2);
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Abstract
With the progress of deep learning technology, the object detection algorithms have achieved good
detection results in general scenes, but they have encountered difficulties in crowded scenes. In crowded scenes,
there are a lot of occlusion between objects, which makes the non-maximum suppression algorithm easy to delete
the correct detection of overlapping objects; at the same time, there are some problems, such as large change of
object scale, small object, insufficient available features and so on. In order to promote the further development
of crowded object detection technology, the related methods and techniques are summarized. Firstly, the research
background and application value of object detection in crowded scenes are introduced. Secondly, the difficulties
of object detection in crowded scenes are discussed, and the defects of object detection algorithm based on
deep learning in crowded scenes are analyzed. Then, the existing object detection algorithms in crowded scenes
are classified and described. Finally, the possible research direction of object detection in crowded scenes is
prospected.
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