Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning

Chi-Won Park, Yuri Seo, Teh-Jen Sun, Ga-Won Lee, E. Huh
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Abstract

Recently, Amazon Web Services (AWS) announced a plan to develop a computer vision artificial intelligence service. The field of computer vision has been receiving constant attention like this, and object detection technology is a very important part in this field of computer vision. Although object detection technology is used in many computer vision fields, object detection technology has many limitations in small object detection and night object detection. Various noise factors degrade the image quality, and it is difficult to expect high accuracy when detecting small objects in this environment. In this paper, we develop a technology that can solve these problems by using multi-modal data. In addition, this object detection technology can be used in various fields by designing and developing a lightweight system that can work well in a low-resource environment.
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基于深度学习的多模态数据小目标检测技术
最近,亚马逊网络服务(AWS)宣布了一项开发计算机视觉人工智能服务的计划。计算机视觉领域就像这样一直受到人们的关注,而目标检测技术是计算机视觉领域中非常重要的一部分。虽然目标检测技术在计算机视觉的许多领域都有应用,但目标检测技术在小目标检测和夜间目标检测方面存在很多局限性。各种噪声因素会降低图像质量,在这种环境下对小目标进行检测时很难达到较高的精度。在本文中,我们开发了一种利用多模态数据来解决这些问题的技术。此外,通过设计和开发能够在低资源环境下良好工作的轻量级系统,该目标检测技术可以应用于各个领域。
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