使用预训练的 CenterNet 和 Yolov3 进行人员检测的速度和精度比较

Friendly, Harizahayu, Zakaria Sembiring
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引用次数: 0

摘要

使用通用设备进行物体检测的方法在速度方面应用并不广泛,而且容易出错。大多数使用 Centernet、Yolov3、Fast-RNN 等已知方法进行速度检测的研究,由于使用的计算机不同,结果也不尽相同。本实验尝试使用 Centernet 和 Yolov3 方法,仅使用常用计算机的 CPU 进行人物检测实验。实验结果表明,Yolov3 的 mAP 点检测精度高达 98.42%,而 Centernet 的 mAP 点检测精度仅为 97%。在处理速度方面,Centernet 的速度要快得多,平均 370 毫秒就能检测到一个人,比 Yolov3 平均 1050 毫秒或 1 秒的速度要好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speed and Accuracy Comparison of Person Detection Using Pretrained CenterNet and Yolov3
Implementations of object detection by using common devices for general purpose application is not widely used for speed and prone to errors. Most of research in speed detection using known method such as Centernet, Yolov3, Fast-RNN has variant result since the computer used are different. This experiment try to conduct experiments for person detection with Centernet and Yolov3 method using commonly used computer only using CPU. Based on the experiments, Yolov3 can give a much better precision for person detection by 98.42% of mAP point while Centernet only 97%. In terms of processing speed, Centernet can give much better speed where it can detect a person in average 370ms better than Yolov3 with average of 1050 ms or 1 second.
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