基于深度学习的图像识别技术研究
摘要
本文研究基于深度学习的图像识别技术中存在的问题，并提出了相应的改进方法。在图像预处理、深度学习算法和特征提取等相关技术概述的基础上，分析了深度学习技术在图像识别中存在的过拟合、标签缺失、物体遮挡和光照不均匀等问题。针对这些问题，针对性地提出数据增强、标签平滑、目标检测技术和多尺度检测等方法，用于改进基于深度学习的图像识别技术，并提高识别准确率和鲁棒性。研究结果表明，这些方法可以有效地缓解基于深度学习的图像识别技术中的问题，提高图像识别准确率。本文对基于深度学习的图像识别技术改进方法的探讨具有一定的参考价值。
关键词： 深度学习 图像识别 过拟合 数据增强
Abstract
This paper studies the problems existing in image recognition technology based on deep learning and proposes corresponding improvement methods. Based on the overview of image preprocessing, deep learning algorithm and feature extraction, the problems of deep learning in image recognition, such as overfitting, label missing, ob ject occlusion and uneven illumination, are analyzed. To solve these problems, data enhancement, label smoothing, ob ject detection and multi-scale detection are proposed to improve the image recognition technology based on deep learning, and improve the recognition accuracy and robustness. The results show that these methods can effectively alleviate the problems in deep learning-based image recognition technology and improve the accuracy of image recognition. This paper has some reference value to discuss the improvement method of image recognition technology based on deep learning.
Key words: Deep learning image recognition overfitting data enhancement
目 录
摘要 I
Abstract II
1绪论 1
1.1 研究背景 1
1.2 研究目的及意义 1
2图像识别技术的相关理论概述 2
2.1 图像预处理 2
2.2 深度学习算法 2
2.3 特征提取 3
3基于深度学习的图像识别技术存在的问题 4
3.1 过拟合 4
3.2 标签缺失 4
3.3 物体遮挡 5
3.4 光照不均匀 5
4基于深度学习的图像识别技术的改进方法 6
4.1 数据增强 6
4.2 标签平滑 7
4.3 目标检测技术 7
4.4 多尺度检测 8
5结论 9
参考文献 10
致谢 11
摘要
本文研究基于深度学习的图像识别技术中存在的问题，并提出了相应的改进方法。在图像预处理、深度学习算法和特征提取等相关技术概述的基础上，分析了深度学习技术在图像识别中存在的过拟合、标签缺失、物体遮挡和光照不均匀等问题。针对这些问题，针对性地提出数据增强、标签平滑、目标检测技术和多尺度检测等方法，用于改进基于深度学习的图像识别技术，并提高识别准确率和鲁棒性。研究结果表明，这些方法可以有效地缓解基于深度学习的图像识别技术中的问题，提高图像识别准确率。本文对基于深度学习的图像识别技术改进方法的探讨具有一定的参考价值。
关键词： 深度学习 图像识别 过拟合 数据增强
Abstract
This paper studies the problems existing in image recognition technology based on deep learning and proposes corresponding improvement methods. Based on the overview of image preprocessing, deep learning algorithm and feature extraction, the problems of deep learning in image recognition, such as overfitting, label missing, ob ject occlusion and uneven illumination, are analyzed. To solve these problems, data enhancement, label smoothing, ob ject detection and multi-scale detection are proposed to improve the image recognition technology based on deep learning, and improve the recognition accuracy and robustness. The results show that these methods can effectively alleviate the problems in deep learning-based image recognition technology and improve the accuracy of image recognition. This paper has some reference value to discuss the improvement method of image recognition technology based on deep learning.
Key words: Deep learning image recognition overfitting data enhancement
目 录
摘要 I
Abstract II
1绪论 1
1.1 研究背景 1
1.2 研究目的及意义 1
2图像识别技术的相关理论概述 2
2.1 图像预处理 2
2.2 深度学习算法 2
2.3 特征提取 3
3基于深度学习的图像识别技术存在的问题 4
3.1 过拟合 4
3.2 标签缺失 4
3.3 物体遮挡 5
3.4 光照不均匀 5
4基于深度学习的图像识别技术的改进方法 6
4.1 数据增强 6
4.2 标签平滑 7
4.3 目标检测技术 7
4.4 多尺度检测 8
5结论 9
参考文献 10
致谢 11
