基于SSD算法的行人检测技术研究

Shan Xiong, Zhang Fan
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引用次数: 2

摘要

人工智能经历了两个寒冬。随着基于深度学习的算法进入人们的视野,目标检测技术也迎来了跨越式的发展。与其他算法相比,SSD算法在检测速度和精度方面具有非常明显的优势。该算法只在顶层进行检测,具有很高的可行性。行人检测中经常会出现背景干扰,同时也存在小目标漏检的问题。本文的主要研究内容有三部分:对原有的SSD算法进行改进,探讨神经网络的基本组成和特点;将原有SSD算法的基本vgg16修改为resnet50,可以提高检测速度和精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Pedestrian Detection Technology Based on SSD Algorithm
Artificial intelligence has experienced two cold winters. As algorithms based on deep learning have entered people's sight, target detection technology has also ushered in a leaping development. Compared with other algorithms, the SSD algorithm has very obvious advantages in detection speed and accuracy. This algorithm only runs detection at the top level, which has very high feasibility. Background interference often occurs in pedestrian detection, and there is also the problem of missed detection of small targets. The main research content of the thesis has three parts: the improvement of the original SSD algorithm and the discussion of the basic composition and characteristics of the neural network; changing the basic vgg16 of the original SSD algorithm to resnet50 can improve the detection speed and accuracy.
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