分析不同阶段的目标检测算法

Qian Wang
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引用次数: 0

摘要

目标检测作为计算机视觉的一部分,在识别领域占有重要地位。它在每个阶段的算法性能都有显著的提高。例如,你只看一次(YOLO)作为目标检测模型似乎具有最大的优势。很明显,它只需要查看一次,就可以识别图像中对象的类别和位置。随着YOLO的不断改进,它的识别速度更快、更准确。本文讨论了不同目标检测算法在每个阶段所表现出的特点和优势。从分析结果来看,YOLO在目标检测方面更有优势。YOLO检测速度快,可以实时处理流媒体视频。与其他算法相比,伪背景检测的数量不到一半,同时具有良好的泛化性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of target detection algorithms at different stages
Target detection as a part of computer vision occupies an important position in the field of recognition. It has seen significant improvements in algorithm performance at every stage. You only look Once (YOLO), for example, seems to have the greatest advantage as a target detection model. It is clear that it only needs to be viewed once to identify the class and location of objects in an image. As YOLO continues to improve, it exhibits even faster and more accurate recognition. This paper discusses the features and advantages shown by the different target detection algorithms at each stage. From the analysis results, YOLO shows more advantages in object detection. YOLO detection is fast and can process streaming video in real-time. Also, the number of false background detections is less than half compared to other algorithms while showing good generalization.
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