使用卷积神经网络进行物体检测

Dr. B. V. Pranay Kumar, P. Rahul, S. Avinash, K. Shyamsundar, B. Sandhya
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引用次数: 0

摘要

近几年来,计算机视觉研究领域出现了明显的增长。在计算机视觉中,物体检测是一项对物体进行分类和定位以便检测出相同物体的任务。广泛应用的物体检测应用包括人机交互、视频监控、卫星图像、运输系统和活动识别。在更广泛的深度学习架构家族中,卷积神经网络(CNN)由一组神经网络层组成,可用于视觉图像。深度 CNN 架构在检测数字图像中的物体方面取得了令人印象深刻的成果。本文全面回顾了使用卷积神经网络进行物体检测的最新发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object Detection using Convolutional Neural Network
During the last years, a noticeable growth is observed in the field of computer vision research. In computer vision, object detection is a task of classifying and localizing the objects in order to detect the same. The widely used object detection applications are human– computer interaction, video surveillance, satellite imagery, transport system, and activity recognition. In the wider family of deep learning architectures, convolutional neural network (CNN) made up with set of neural network layers is used for visual imagery. Deep CNN architectures exhibit impressive results for detection of objects in digital image. This paper represents a comprehensive review of the recent development in object detection using convolutional neural networks
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