基于改进卷积神经网络的图像识别技术研究
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河北科技大学 电气工程学院

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TP273

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国家国防基础项目(JCKY***);河北省重点研发项目(192***D);河北省高等学校科学技术研究项目(BJ2017041)


Research on Image Recognition Technology Based on Improved Convolutional Neural Network
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School of Electrical Engineering,Hebei University of Science and Technology,Shijiazhuang

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    摘要:

    随着无人机的快速发展,图像识别技术在无人机飞行中就显得尤为重要。本论文针对所采集到的图像,先进行灰度化处理,在之后采用高斯金字塔算法,构建高斯差分金字塔,并在其基础上进一步结合高斯模糊算法,对图像进行增强处理以及初步提取目标图像的特征点,并结合卷积神经网络模型,进行图像的二次特征提取以及图像的识别分类。并与未经灰度化处理的彩色图像以及只进行灰度化处理的卷积神经网络模型进行对比,经过仿真验证后,此算法识别准确率更高,而且响应时间更短。从而能够使无人机在飞行过程中能够快速有效辨认障碍物的种类,及时采取相应的避让方法,减少危险事故的发生。

    Abstract:

    With the rapid development of UAV, image recognition technology is particularly important in uav flight. This paper for the collected images, to gray, after using the gaussian pyramid algorithm, build the gaussian pyramid of difference, and on the basis of the further combining gaussian fuzzy algorithm, the image enhancement processing as well as the preliminary extraction of target image feature points, and connecting with the convolutional neural network model, image of the secondary classification feature extraction and image recognition. Compared with the color image without graying processing and the convolutional neural network model only with graying processing, the algorithm has higher recognition accuracy and shorter response time after simulation verification. In this way, the uav can quickly and effectively identify the types of obstacles and take corresponding avoidance methods in time to reduce the occurrence of dangerous accidents.

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历史
  • 收稿日期:2020-06-16
  • 最后修改日期:2020-06-16
  • 录用日期:2023-09-20
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