Along with the rapid development of cloud computing, IoT, and AI technologies, cloud video surveillance (CVS) has become a hotly discussed topic, especially when facing the requirement of real-time analysis in smart applications. Object detection usually plays an important role for environment monitoring and activity tracking in surveillance system. The emerging edge-cloud computing paradigm provides us an opportunity to deal with the continuously generated huge amount of surveillance data in an on-site manner across IoT systems. However, the detection performance is still far away from satisfactions due to the complex surveilling environment. In this study, we focus on the multitarget detection for real-time surveillance in smart IoT systems. A newly designed deep neural network model called A-YONet, which is constructed by combining the advantages of YOLO and MTCNN, is proposed to be deployed in an end–edge–cloud surveillance system, in order to realize the lightweight training and feature learning with limited computing sources. An intelligent detection algorithm is then developed based on a preadjusting scheme of anchor box and a multilevel feature fusion mechanism. Experiments and evaluations using two data sets, including one public data set and one homemade data set obtained in a real surveillance system, demonstrate the effectiveness of our proposed method in enhancing training efficiency and detection precision, especially for multitarget detection in smart IoT application developments.

中文翻译：

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深度学习增强型多目标检测，用于智能物联网中的端-边缘-云监控

随着云计算、物联网和人工智能技术的快速发展，云视频监控（CVS）成为一个热门话题，尤其是在智能应用中面临实时分析的需求时。目标检测通常在监控系统中的环境监测和活动跟踪中起着重要作用。新兴的边缘云计算范式为我们提供了一个机会，可以跨物联网系统以现场方式处理不断生成的大量监控数据。然而，由于监控环境复杂，检测性能还远不能令人满意。在这项研究中，我们专注于智能物联网系统中实时监控的多目标检测。一种新设计的深度神经网络模型，称为 A-YONet，结合YOLO和MTCNN的优点构建的，建议部署在端-边-云监控系统中，以实现有限计算资源的轻量级训练和特征学习。然后基于anchor box的预调整方案和多级特征融合机制开发了一种智能检测算法。使用两个数据集的实验和评估，包括一个公共数据集和一个在真实监控系统中获得的自制数据集，证明了我们提出的方法在提高训练效率和检测精度方面的有效性，特别是对于智能物联网应用开发中的多目标检测。以实现有限计算资源的轻量级训练和特征学习。然后基于anchor box的预调整方案和多级特征融合机制开发了一种智能检测算法。使用两个数据集的实验和评估，包括一个公共数据集和一个在真实监控系统中获得的自制数据集，证明了我们提出的方法在提高训练效率和检测精度方面的有效性，特别是对于智能物联网应用开发中的多目标检测。以实现有限计算资源的轻量级训练和特征学习。然后基于anchor box的预调整方案和多级特征融合机制开发了一种智能检测算法。使用两个数据集的实验和评估，包括一个公共数据集和一个在真实监控系统中获得的自制数据集，证明了我们提出的方法在提高训练效率和检测精度方面的有效性，特别是对于智能物联网应用开发中的多目标检测。