Using Eye-Tracking to Measure Worker Situation Awareness in Augmented Reality

Wu, Shaoze; Chen, Haosen; Hou, Lei; Zhang, Guomin (Kevin); Li, Chun-Qing

doi:10.1016/j.autcon.2024.105582

Cited by 2 publications

References 55 publications

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Leveraging deep learning techniques for condition assessment of stormwater pipe network

Yussuf,

Weerasinghe,

Chen

et al. 2024

J Civil Struct Health Monit

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Inspections and condition monitoring of the stormwater pipe networks have become increasingly crucial due to their vast geographical span and complex structure. Unmanaged pipelines present significant risks, such as water leakage and flooding, posing threats to urban infrastructure. However, only a small percentage of pipelines undergo annual inspections. The current practice of CCTV inspections is labor-intensive, time-consuming, and lacks consistency in judgment. Therefore, this study aims to propose a cost-effective and efficient semi-automated approach that integrates computer vision technology with Deep Learning (DL) algorithms. A DL model is developed using YOLOv8 with instance segmentation to identify six types of defects as described in Water Services Association (WSA) Code of Australia. CCTV footage from Banyule City Council was incorporated into the model, achieving a mean average precision (mAP@0.5) of 0.92 for bounding boxes and 0.90 for masks. A cost–benefit analysis is conducted to assess the economic viability of the proposed approach. Despite the high initial development costs, it was observed that the ongoing annual costs decreased by 50%. This model allowed for faster, more accurate, and consistent results, enabling the inspection of additional pipelines each year. This model serves as a tool for every local council to conduct condition monitoring assessments for stormwater pipeline work in Australia, ultimately enhancing resilient and safe infrastructure asset management.

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Leveraging deep learning techniques for condition assessment of stormwater pipe network

Yussuf,

Weerasinghe,

Chen

et al. 2024

J Civil Struct Health Monit

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Personal Protective Equipment Detection for Industrial Environments: A Lightweight Model Based on RTDETR for Small Targets

Wang,

Ma,

Chen

et al. 2024

Preprint

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Existing Personal Protective Equipment (PPE) detection research typically focuses on close-range scenarios, often neglecting small target detection in industrial surveillance. To address this gap, we propose LMD-RTDETR, a lightweight algorithm for small PPE targets. In the encoding stage of the neural network, we incorporated an Adaptive Inductive Frequency Learnable Position Encoding (AIFI-LPE) structure to enhance the model's ability to understand complex scenes. Additionally, the Dynamic Group Shuffle Transformer SlimNeck (DGST-SlimNeck) module and Multi-Path Spatial Semantic Feature Fusion (MP-SSFF) structure are optimized in the Neck network, enhancing the model's feature learning ability and achieving multi-scale feature fusion. These innovations significantly improve detection accuracy for small objects in complex scenes. We conducted extensive experiments on both a custom PPE dataset and the public VisDrone dataset. Compared to RT-DETR-r18 on the PPE dataset, LMD-RTDETR shows a 2.4% improvement in mean Average Precision at Intersection over Union thresholds from 50% to 95% (mAP@50:95). Simultaneously, it reduces parameters by 24.2% and computational complexity (Giga Floating Point Operations per Second, GFLOPs) by 6.8%. On the VisDrone dataset, it achieves an mAP@50 of 39.0%, demonstrating strong generalization capabilities. These results highlight LMD-RTDETR's effectiveness in small-target PPE detection within industrial settings, offering a balance between high accuracy and computational efficiency. Our work contributes to enhancing workplace safety through improved automated PPE detection, particularly in complex industrial environments with diverse monitoring conditions.

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Using Eye-Tracking to Measure Worker Situation Awareness in Augmented Reality

Cited by 2 publications

References 55 publications

Leveraging deep learning techniques for condition assessment of stormwater pipe network

Leveraging deep learning techniques for condition assessment of stormwater pipe network

Personal Protective Equipment Detection for Industrial Environments: A Lightweight Model Based on RTDETR for Small Targets

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