Author name cluster

Qing Han

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4 papers

2 author rows

IS Journal 2025 Journal Article

Edge–Cloud Collaborative Real-Time Video Object Detection for Industrial Surveillance Systems

Siyan Guo
Cong Zhao
Shusen Yang
Yingying Liang
Yimeng Wang
Qing Han

Industrial video surveillance systems play a pivotal role in smart industry, prioritizing safety protection. Object detection resorting to deep neural networks (DNNs) is promising in achieving accurate and autonomous localization and identification of anomalies in video frames, supporting broad intelligent video surveillance applications. However, existing approaches are either computation- or communication-intensive. Limited by the constrained resources of industrial systems, they usually suffer from a high end-to-end (E2E) latency, and cannot be directly applied to latency-sensitive applications. In this article, we present a light-weight edge–cloud collaborative branchy DNN, CombiNet, and customize an intelligent edge device, Edge–Vbox, to construct an effective real-time video object detection solution. In our case study of intelligent smart grid substation operation and maintenance, experimental results using real-world data demonstrate that our approach significantly outperforms state-of-the-art methods in E2E latency, and manages to achieve real-time video object detection with negligible accuracy loss.

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IS Journal 2023 Journal Article

ECCVideo: A Scalable Edge Cloud Collaborative Video Analysis System

Qing Han
Xuebin Ren
Peng Zhao
Yimeng Wang
Luhui Wang
Cong Zhao
Xinyu Yang

Video analysis drives a wide range of applications in the fields of public safety, autonomous vehicles, etc. , with the great potential to impact society. Traditional cloud-based approaches are not applicable because of prohibitive bandwidth consumption and high response latency, while simply edge-based video analysis suffers from large computation delay, considering the restricted computing capacity of edge servers. Therefore, in this article, we focus on low-latency edge-cloud collaborative video analytic applications (ECCVApps) by making full use of resources at both the edge and cloud. Particularly, we present an edge-cloud collaborative video analysis system called ECCVideo, to support the unified management of heterogeneous servers and facilitate the development and deployment of large-scale ECCVApps. Under ECCVideo, we design the application architecture of ECCVApps, including presentation paradigm, transparent communication services, and full lifecycle management. To validate the proposed system, a real-time object detection application is deployed on the ECCVideo prototype.

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EAAI Journal 2023 Journal Article

SAR ship localization method with denoising and feature refinement

Cheng Zha
Weidong Min
Qing Han
Wei Li
Xin Xiong
Qi Wang
Meng Zhu

Synthetic Aperture Radar (SAR) ship detection is greatly important to marine transportation monitoring and fishery resource management. To improve the detection accuracy of small ships, an SAR ship localization method with Denoising and Feature Refinement (DFR) is proposed in this paper. It consists of three parts. The first part is the denoising module, which uses non-local mean to suppress the speckle noise of the SAR image. The second part is Hierarchical Feature Fusion (HFF) module. It can integrate more low-level features by adding skip connections. This prevents the low-level spatial position information of the fused features from being diluted by high-level semantic information, therefore it is beneficial to the detection of small ships. The third part is a center-based ship predictor with Feature Refinement (FR). The FR module is proposed to refine the features and reduce the background interference, which is conducive to locate ships more accurately. Extensive experiments are conducted. The experimental results show that after adding the denoising and FR modules, the value of AP 0. 5 is increased by 1. 7% and 2. 3%, respectively, which proves the effectiveness of these two modules. In inshore and offshore scenarios, the AP 0. 5 values of DFR are 0. 884 and 0. 966, respectively, achieving the best results. The proposed method can also be generalized to mark lesion locations in medical images and detect offshore oil production platforms.

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NMR Workshop 2004 Conference Paper

Paraconsistent default reasoning

Qing Han
Zuoquan Lin

In this paper, a novel technique called bi-default theory is proposed for handling inconsistent knowledge simultaneously in the context of default logic without leading to triviality of the extension. To this end, the positive and negative transformations of propositional formulas are defined such that the semantic link between a literal and its negation is split. It is proven that the bi-default theory preserves many nice properties of Reiter’s original theory and guarantees the existence of consistent modified bi-extensions. Thus, the bi-default logic is a generalization of default logic in the presence of inconsistency. Furthermore, a method is provided as an alternative approach for making the reasoning ability of paraconsistent logic as powerful as classical one.

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