3D Object Detection and Localization within Healthcare Facilities DOI
Da Hu, Mengjun Wang,

Shuai Li

и другие.

2018 Winter Simulation Conference (WSC), Год журнала: 2023, Номер unknown, С. 2710 - 2721

Опубликована: Дек. 10, 2023

This study introduces a deep learning-based method for indoor 3D object detection and localization in healthcare facilities. incorporates spatial channel attention mechanisms into the YOLOv5 architecture, ensuring balance between accuracy computational efficiency. The network achieves an AP50 of 67.6%, mAP 46.7%, real-time rate with FPS 67. Moreover, proposes novel mechanism estimating coordinates detected objects projecting them onto maps, average error 0.24 m 0.28 x y directions, respectively. After being tested validated real-world data from university campus, proposed shows promise improving disinfection efficiency facilities by enabling robot navigation.

Язык: Английский

Automated detection of vehicles with anomalous trajectories in traffic surveillance videos DOI
José David Fernández-Rodríguez, Jorge García-González, Rafaela Benítez-Rochel

и другие.

Integrated Computer-Aided Engineering, Год журнала: 2023, Номер 30(3), С. 293 - 309

Опубликована: Март 21, 2023

Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and accidents. In this respect, it crucial detect those anomalous vehicle trajectories, that is, trajectories depart usual paths. work, model proposed automatically address by using video sequences cameras. The proposal detects vehicles frame frame, tracks their across frames, estimates velocity vectors, compares them vectors other spatially adjacent trajectories. From comparison very different (anomalous) neighboring detected. practical terms, strategy wrong-way Some components off-the-shelf, such as detection provided recent deep learning approaches; however, several options considered analyzed tracking. performance system has been tested with wide range real synthetic videos.

Язык: Английский

Процитировано

9

Multi-classifier information fusion for human activity recognition in healthcare facilities DOI
Da Hu, Mengjun Wang, Shuai Li

и другие.

Frontiers of Engineering Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0

UV‐C Disinfection Robots: A Systematic Review DOI Creative Commons
Sergio Genilson Pfleger, Maryah Elisa Morastoni Haertel, Patricia Della Méa Plentz

и другие.

Journal of Field Robotics, Год журнала: 2025, Номер unknown

Опубликована: Май 8, 2025

ABSTRACT The use of ultraviolet (UV‐C) disinfection robots has become increasingly popular in diverse settings, including hospitals, schools, public transportation, and high‐traffic areas, especially following the COVID‐19 pandemic. These offer potential to enhance efficiency reduce human exposure microorganisms. However, application UV‐C light for is not without challenges. challenges include need precise environmental mapping, accurate dose delivery, mitigation safety risks associated with humans animals. This systematic review aims examine current development robots, identify key technological challenges, explore methods used ensure effective safe disinfection. An automated search was conducted Scopus, IEEE Xplore, ACM Digital Library, SpringerLink studies published up July 2023, followed by snowballing gather additional relevant works. A total 96 were reviewed. majority these either did address correct UVGI or lacked appropriate delivery. Additionally, positioning lamps often done subjectively, most incorporate any measures prevent accidents related exposure. Based on this analysis, a new classification proposed, highlighting advancements readiness levels. Despite progress made field, significant remain developing that deliver doses while ensuring operational efficiency. emphasizes further research gaps, particularly concerning navigation algorithms, accuracy, measures.

Язык: Английский

Процитировано

0

Segmentation and Tracking of Moving Objects on Dynamic Construction Sites DOI
Da Hu,

Sultan Al Shafian

Construction Research Congress 2022, Год журнала: 2024, Номер unknown, С. 60 - 69

Опубликована: Март 18, 2024

Язык: Английский

Процитировано

2

Training of construction robots using imitation learning and environmental rewards DOI Creative Commons
Kangkang Duan, Zhengbo Zou, T.Y. Yang

и другие.

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 13, 2024

Abstract Construction robots are challenging the paradigm of labor‐intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling to mimic expert actions. However, obtaining high‐quality demonstrations is major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In paper, two innovations have been proposed. First, traditional control using controllers has replaced vision‐based hand gesture for intuitive demonstration collection. Second, novel method that integrates both and simple environmental rewards proposed strike balance between imitation exploration. To achieve goal, two‐step training first step, an collection platform virtual reality utilized. algorithm used train policy Experimental results demonstrate combining IL can significantly accelerate training, even limited data.

Язык: Английский

Процитировано

2

Deep deterministic policy gradient with constraints for gait optimisation of biped robots DOI
Xingyang Liu, Haina Rong, Ferrante Neri

и другие.

Integrated Computer-Aided Engineering, Год журнала: 2023, Номер 31(2), С. 139 - 156

Опубликована: Дек. 19, 2023

In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on topic highlighted the limitations controller’s ability accurately track foot placement discrete terrains lack of consideration safety concerns. study, address these challenges by focusing ensuring overall system’s safety. To begin with, tackle inverse kinematics problem introducing constraints damping least squares method. This enhancement not only addresses singularity issues but also guarantees safe ranges joint angles, thus stability reliability system. Based this, adoption DDPG method correct controller deviations. DDPG, incorporate constraint layer into Actor network, incorporating deviations as state inputs. By conducting offline training within range it serves corrector. Lastly, validate effectiveness our proposed approach dynamic simulations using CRANE robot. Through comprehensive assessments, including analysis, evaluation, experiments terrains, demonstrate superiority practicality enhancing performance while Overall, contributes advancement robot locomotion addressing gait optimisation from multiple perspectives, handling, constraints, learning.

Язык: Английский

Процитировано

4

Integrated Framework for Bridge Crack Detection and Semantic BIM Model Generation Using Drone-Captured Imagery and Deep Learning Techniques DOI
Da Hu, Tien Yee

Опубликована: Ноя. 14, 2023

Concrete cracking in bridges significantly endangers their safety and integrity. Traditional crack detection methods, reliant on human visual inspection, are labor-intensive prone to errors. This paper introduces a unique framework for bridge integration with building information models (BIM), trialed 423-ft Atlanta, Georgia. The comprises two main stages: (1) creating BIM model using drone-captured images structure from motion (SFM) photogrammetry, (2) utilizing deep learning-based encoder-decoder network segment cracks orthomosaic superimpose these segmented onto the model. suggested method showed robust performance, achieving mean intersection over union (mIoU) of 0.787, precision 0.751, recall 0.742. These results underline potential proposed improve efficiency inspection processes.

Язык: Английский

Процитировано

1

UV-C Disinfection Robots: A Systematic Review DOI Open Access
Sergio Genilson Pfleger, Maryah Elisa Morastoni Haertel, Patricia Della Méa Plentz

и другие.

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Июнь 6, 2024

Several room disinfection robots using Ultraviolet C (UV-C) light have emerged recently, especially with the COVID-19 outbreak. This systematic review aims to identify current development status of Germicidal Irradiation (UVGI) robots, their limitations, and technologies they use. An automated search was performed on Scopus, ACM Digital Library, IEEE Xplore, SpringerLink platforms for papers up July 2023, followed by a snowballing search. Were found 96 studies, which majority were not concerned dose UVGI applied or did implement any technique deliver appropriate dose; positioning lamps carried out subjectively; most works prevent accidents UVGI. From analysis these it possible propose novel classification different types based technological readiness levels. The data shows that despite recent advances, is still early, many advances be made.

Язык: Английский

Процитировано

0

Urban Subsurface Mapping via Deep Learning Based GPR Data Inversion DOI
Mengjun Wang, Da Hu, Shuai Li

и другие.

2018 Winter Simulation Conference (WSC), Год журнала: 2022, Номер unknown, С. 2440 - 2450

Опубликована: Дек. 11, 2022

Accurate mapping of urban subsurface is essential for managing underground infrastructure and preventing excavation accidents. Ground-penetrating radar (GPR) a non-destructive test method that has been used extensively to locate utilities. However, existing approaches are not able retrieve detailed utility information (e.g., material dimensions) from GPR scans. This research aims automatically detect characterize buried utilities with location, dimension, by processing To achieve this aim, inverting data based on deep learning developed directly reconstruct the permittivity maps cross-sectional profiles structure corresponding A large number synthetic scans ground-truth labels were generated train inversion network. The experiment results indicated proposed achieved Mean Absolute Error 0.53, Structural Similarity Index Measure 0.91, an $R^{2}$ 0.96.

Язык: Английский

Процитировано

1

3D Object Detection and Localization within Healthcare Facilities DOI
Da Hu, Mengjun Wang,

Shuai Li

и другие.

2018 Winter Simulation Conference (WSC), Год журнала: 2023, Номер unknown, С. 2710 - 2721

Опубликована: Дек. 10, 2023

This study introduces a deep learning-based method for indoor 3D object detection and localization in healthcare facilities. incorporates spatial channel attention mechanisms into the YOLOv5 architecture, ensuring balance between accuracy computational efficiency. The network achieves an AP50 of 67.6%, mAP 46.7%, real-time rate with FPS 67. Moreover, proposes novel mechanism estimating coordinates detected objects projecting them onto maps, average error 0.24 m 0.28 x y directions, respectively. After being tested validated real-world data from university campus, proposed shows promise improving disinfection efficiency facilities by enabling robot navigation.

Язык: Английский

Процитировано

0