摘 要
随着信息技术的迅猛发展，安防领域对图像识别技术的需求日益增长。基于深度学习的图像识别系统凭借其强大的特征提取和模式识别能力，在安防监控、人员识别等方面展现出巨大潜力。本研究旨在探讨深度学习算法在安防图像识别中的应用效果，通过构建卷积神经网络模型，针对不同场景下的安防图像进行训练与优化。实验采用大规模真实场景数据集，涵盖多种复杂环境条件，包括光照变化、遮挡情况等，以确保模型的鲁棒性和泛化能力。研究结果表明，该系统能够有效提高目标检测精度，降低误报率，尤其在人脸识别、车辆识别等关键任务中表现优异。创新点在于引入了注意力机制，使模型可以聚焦于图像中最具辨识度的区域，从而进一步提升识别准确率。
关键词：深度学习 图像识别 安防监控
Abstract
With the rapid development of information technology, the demand for image recognition technology in the security field is increasing. The image recognition system based on deep learning shows great potential in security monitoring and personnel recognition with its powerful feature extraction and pattern recognition capabilities. The purpose of this study is to explore the application effect of deep learning algorithm in security image recognition, and to build a convolutional neural network model. The experiment uses a large-scale real scene dataset, covering a variety of complex environmental conditions, including light changes, occlusion conditions, etc., to ensure the robustness and generalization ability of the model. The results show that the system can effectively improve the accuracy of target detection and reduce the false alarm rate, especially in face recognition, vehicle recognition and other key tasks. The innovation is the introduction of the attention mechanism, so that the model can focus on the most recognizable area in the image, so as to further improve the recognition accuracy.
Keyword:Deep Learning Image Recognition Security Surveillance
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2深度学习图像识别技术基础 2
2.1深度学习算法原理 2
2.2图像识别关键技术 3
2.3安防领域特殊需求 3
3安防场景下的应用挑战 4
3.1复杂环境适应性 4
3.2实时处理要求 4
3.3数据安全与隐私保护 5
4应用案例与效果评估 6
4.1典型应用场景分析 6
4.2系统性能评测指标 6
4.3实际应用效果评价 7
结论 7
参考文献 9
致谢 10
随着信息技术的迅猛发展，安防领域对图像识别技术的需求日益增长。基于深度学习的图像识别系统凭借其强大的特征提取和模式识别能力，在安防监控、人员识别等方面展现出巨大潜力。本研究旨在探讨深度学习算法在安防图像识别中的应用效果，通过构建卷积神经网络模型，针对不同场景下的安防图像进行训练与优化。实验采用大规模真实场景数据集，涵盖多种复杂环境条件，包括光照变化、遮挡情况等，以确保模型的鲁棒性和泛化能力。研究结果表明，该系统能够有效提高目标检测精度，降低误报率，尤其在人脸识别、车辆识别等关键任务中表现优异。创新点在于引入了注意力机制，使模型可以聚焦于图像中最具辨识度的区域，从而进一步提升识别准确率。
关键词：深度学习 图像识别 安防监控
Abstract
With the rapid development of information technology, the demand for image recognition technology in the security field is increasing. The image recognition system based on deep learning shows great potential in security monitoring and personnel recognition with its powerful feature extraction and pattern recognition capabilities. The purpose of this study is to explore the application effect of deep learning algorithm in security image recognition, and to build a convolutional neural network model. The experiment uses a large-scale real scene dataset, covering a variety of complex environmental conditions, including light changes, occlusion conditions, etc., to ensure the robustness and generalization ability of the model. The results show that the system can effectively improve the accuracy of target detection and reduce the false alarm rate, especially in face recognition, vehicle recognition and other key tasks. The innovation is the introduction of the attention mechanism, so that the model can focus on the most recognizable area in the image, so as to further improve the recognition accuracy.
Keyword:Deep Learning Image Recognition Security Surveillance
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2深度学习图像识别技术基础 2
2.1深度学习算法原理 2
2.2图像识别关键技术 3
2.3安防领域特殊需求 3
3安防场景下的应用挑战 4
3.1复杂环境适应性 4
3.2实时处理要求 4
3.3数据安全与隐私保护 5
4应用案例与效果评估 6
4.1典型应用场景分析 6
4.2系统性能评测指标 6
4.3实际应用效果评价 7
结论 7
参考文献 9
致谢 10
