使用YOLO算法改进人脸检测的影响性能

Rakha Asyrofi, Yoni Azhar Winata
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引用次数: 2

摘要

图像数据增强是一种在不实际收集新数据的情况下增加可用数据多样性的方法。在本研究中,研究人员评估了撒切尔效应、双重错觉和反演等图像处理在人脸检测性能方面的应用,以满足数据增强需求,其中获得的数据有一个缺点,即用于创建训练模型的数据量有限。本研究的目的是增加数据的多样性,以便在给定其他类似数据集的情况下做出正确的预测。为了对图像进行人脸检测,使用YOLOv3完成,然后比较添加数据增强后和之前数据集的准确性结果。
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The Improvement Impact Performance of Face Detection Using YOLO Algorithm
Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.
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