基于进化计算的图像处理自监督学习:用于多光谱物体检测的特征提取和融合的大数据驱动方法

IF 8.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Xiaoyang Shen, Haibin Li, Achyut Shankar, Wattana Viriyasitavat, Vinay Chamola
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引用次数: 0

摘要

图像物体识别与检测技术在很多场景中都有广泛应用。近年来,大数据日益丰富,大数据驱动的人工智能模型越来越受到关注。进化计算也为深度学习模型的优化和改进提供了强大的驱动力。本文提出了一种基于自监督和数据驱动学习的图像物体检测方法。与其他方法不同的是,我们的方法创新性地使用了多光谱数据融合和进化计算来优化模型。具体来说,我们的方法独特地结合了可见光图像和红外图像来检测和识别图像目标。首先,我们利用自监督学习方法和 AutoEncoder 模型对两类图像进行高维特征提取。其次,我们融合从可见光和红外图像中提取的特征来检测和识别目标。第三,我们利用进化学习算法引入了一种模型参数优化方法,以提高模型性能。在公共数据集上的验证表明,我们的方法取得了与现有方法相当甚至更优的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection

Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods.

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来源期刊
Journal of Big Data
Journal of Big Data Computer Science-Information Systems
CiteScore
17.80
自引率
3.70%
发文量
105
审稿时长
13 weeks
期刊介绍: The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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