Intelligent network is crucial in the building of telecom networks because it utilizes artificial intelligent technologies to improve the performance. Salient object detection has increasingly attracted interest from intelligent network research since estimating human attention to objects is a crucial step in various surveillance applications. However, the computational-consuming and memory-consuming detection model is still less effective when it is deployed only either on the cloud or on the edge. In this article, we propose a specially-designed cloud-edge distributed framework for salient object detection based on the intelligent network. This framework can overcome the difficulty to transmit massive data in the cloud-only deployment scheme, as well as the difficulty to analyze massive data in the edge-only deployment scheme. However, the traditional cloud-edge distributed schemes are unsuitable to salient object detection task because of two challenges: 1) balance between the within-semantic knowledge and cross-semantic knowledge for the model training in different servers; 2) contradiction between extracting the semantic knowledge with global contextual information and local detailed information. To tackle the first challenge, our framework enables a hierarchical information allocation strategy in the cloud. It can prompt the salient object detection model in the edge to learn more from the similar scenes or semantics with where the edge server is located, while preserving the generalization ability of the model in the different scenes. To address the second challenge, our framework proposes a novel pyramidal deep learning model. It can effectively capture the global contextual features of the salient object, while preserving its local detailed features. The extensive experiments performed on six commonly- used public datasets can demonstrate the effectiveness of our framework and its superiority to 11 state-of-the-art approaches.

中文翻译：

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分布式云边智能网络中的显着目标检测


智能网络在电信网络建设中至关重要，因为它利用人工智能技术来提高性能。显着目标检测越来越引起智能网络研究的兴趣，因为估计人类对目标的注意力是各种监控应用中的关键步骤。然而，消耗计算和消耗内存的检测模型在仅部署在云端或边缘时仍然效果较差。在本文中，我们提出了一种专门设计的基于智能网络的云边分布式显着目标检测框架。该框架可以克服纯云部署方案中海量数据传输的困难，以及纯边缘部署方案中海量数据分析的困难。然而，传统的云边分布式方案不适合显着目标检测任务，因为存在两个挑战：1）不同服务器上模型训练的内语义知识和跨语义知识之间的平衡； 2）利用全局上下文信息提取语义知识与局部详细信息之间的矛盾。为了应对第一个挑战，我们的框架在云中实现了分层信息分配策略。它可以促使边缘的显着目标检测模型从与边缘服务器所在的相似场景或语义中学习更多信息，同时保留模型在不同场景中的泛化能力。为了解决第二个挑战，我们的框架提出了一种新颖的金字塔深度学习模型。它可以有效地捕获显着对象的全局上下文特征，同时保留其局部细节特征。 在六个常用公共数据集上进行的广泛实验可以证明我们的框架的有效性及其相对于 11 种最先进方法的优越性。