School of Software, Jiangxi Normal University, Nanchang 330022, China;
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China.
Sensors (Basel). 2020 May 18;20(10):2866. doi: 10.3390/s20102866.
As the Internet of Things (IoT) is predicted to deal with different problems based on big data, its applications have become increasingly dependent on visual data and deep learning technology, and it is a big challenge to find a suitable method for IoT systems to analyze image data. Traditional deep learning methods have never explicitly taken the color differences of data into account, but from the experience of human vision, colors play differently significant roles in recognizing things. This paper proposes a weight initialization method for deep learning in image recognition problems based on RGB influence proportion, aiming to improve the training process of the learning algorithms. In this paper, we try to extract the RGB proportion and utilize it in the weight initialization process. We conduct several experiments on different datasets to evaluate the effectiveness of our proposal, and it is proven to be effective on small datasets. In addition, as for the access to the RGB influence proportion, we also provide an expedient approach to get the early proportion for the following usage. We assume that the proposed method can be used for IoT sensors to securely analyze complex data in the future.
随着物联网（IoT）预计将根据大数据处理不同的问题，其应用越来越依赖于视觉数据和深度学习技术，因此为物联网系统找到一种合适的方法来分析图像数据是一个巨大的挑战。传统的深度学习方法从未明确考虑数据的颜色差异，但从人类视觉的经验来看，颜色在识别事物方面起着不同的重要作用。本文提出了一种基于 RGB 影响比例的图像识别问题深度学习权重初始化方法，旨在改进学习算法的训练过程。在本文中，我们尝试提取 RGB 比例并将其用于权重初始化过程。我们在不同的数据集上进行了几次实验来评估我们的建议的有效性，并且在小数据集上证明是有效的。此外，对于访问 RGB 影响比例，我们还提供了一种权宜之计，以获取后续使用的早期比例。我们假设所提出的方法可以用于物联网传感器来安全地分析未来复杂的数据。
