R-CNN与Fast R-CNN在图像分析中的区别:性能比较

Maad M. Mijwil, Karan Aggarwal, Ruchi Doshi, K. Hiran, Murat Gök
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引用次数: 10

摘要

深度学习技术在现代许多领域都变得至关重要,因为它们擅长分析和预测真实的大数据,以便在不同情况下采取行动。虽然它在很多方面都是不可思议的,但它容易对数据产生误解,因此在数据分析的执行阶段跟进时,不可缺少经验丰富的专家团队。卷积神经网络是最重要的深度学习技术之一。它广泛应用于视觉图像分析。本文对R-CNN和Fast R-CNN进行了总结和比较,两者在图像分析方面是最好的。本文的结论是Fast R-CNN在测试和训练中最适合的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison
Deep learning techniques have become vital in many fields in the modern era because they are excellent at analysing and predicting real big data to act in different situations. Although it is marvellous in many aspects, it is prone to misinterpretation of data, so teams of experienced specialists cannot be dispensed with in following up on the execution stages of data analysis. Convolutional Neural Network is one of the most significant deep learning techniques. It is widely employed in visual image analysis. In this article, R-CNN and Fast R-CNN are summarised and compared and are the best in image analysis. This article concluded that the most suitable performance is for Fast R-CNN in testing and training.
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