Phat Tai Lam, Jaehyuk Lee, Yunwoo Lee, Xuan Tinh Nguyen, Van Vy, Kevin Han, Hyungchul Yoon
With the rapid increase in the number of vehicles, accurately identifying vehicle loads is crucial for maintaining and operating transportation infrastructure systems. Existing load identification methods typically rely on collecting vehicle load data from weigh-in-motion (WIM) systems when vehicles pass over them. However, cumbersome installation, high costs, and regular maintenance are the main obstacles that prevent WIM from being widely used in practice. This study introduces the visual WIM (V-WIM) framework, a vision-based approach for tracking and identifying moving loads. The V-WIM framework consists of two main components, the vehicle weight estimation and the vehicle tracking and location estimation. Vehicle weight is estimated using tire deformation parameters extracted from tire images through object detection and optical character recognition techniques. A deep learning-based YOLOv8 algorithm is employed as a vehicle detector, combined with the ByteTrack algorithm for tracking vehicle location. The vehicle weight and its corresponding location are then integrated to enable simultaneous vehicle weight estimation and tracking. The performance of the proposed framework was evaluated through two component validation tests and one on-site validation test, demonstrating its capability to overcome the limitations of existing methods.
{"title":"A vision-based weigh-in-motion approach for vehicle load tracking and identification","authors":"Phat Tai Lam, Jaehyuk Lee, Yunwoo Lee, Xuan Tinh Nguyen, Van Vy, Kevin Han, Hyungchul Yoon","doi":"10.1111/mice.13461","DOIUrl":"10.1111/mice.13461","url":null,"abstract":"<p>With the rapid increase in the number of vehicles, accurately identifying vehicle loads is crucial for maintaining and operating transportation infrastructure systems. Existing load identification methods typically rely on collecting vehicle load data from weigh-in-motion (WIM) systems when vehicles pass over them. However, cumbersome installation, high costs, and regular maintenance are the main obstacles that prevent WIM from being widely used in practice. This study introduces the visual WIM (V-WIM) framework, a vision-based approach for tracking and identifying moving loads. The V-WIM framework consists of two main components, the vehicle weight estimation and the vehicle tracking and location estimation. Vehicle weight is estimated using tire deformation parameters extracted from tire images through object detection and optical character recognition techniques. A deep learning-based YOLOv8 algorithm is employed as a vehicle detector, combined with the ByteTrack algorithm for tracking vehicle location. The vehicle weight and its corresponding location are then integrated to enable simultaneous vehicle weight estimation and tracking. The performance of the proposed framework was evaluated through two component validation tests and one on-site validation test, demonstrating its capability to overcome the limitations of existing methods.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 11","pages":"1466-1489"},"PeriodicalIF":8.5,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13461","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drop brackets (DB) and swivel clevises (SC) are critical components of railway catenary systems, playing a key role in maintaining cantilever stability. The condition of these components significantly impacts the safe operation of the catenary, necessitating periodic inspections to detect defects. This task is typically performed by onboard cameras using computer vision. However, traditional image processing methods often focus on shallow features, making it difficult to handle the interference from complex structures of components. While deep learning methods have strong capabilities in capturing semantic features, the lack of crack samples makes reliable crack identification challenging. Therefore, a joint approach for crack detection based on structural perception is proposed. The approach integrates three main components: object structure perception, stick structure perception, and crack defect detection. A multistream catenary components segmentation network (MCSnet) is employed to extract structural features of the DB and SC. Subsequently, an adaptive stick perception method (ASPM) is applied to identify potential crack candidates based on stick structure. The combined structural features enable effective detection of crack defects. Experimental results validate the effectiveness of the proposed approach.
{"title":"A crack detection method based on structure perception for drop brackets and swivel clevises in catenary system","authors":"Dongkai Zhang, Lifan Sun, Ferrante Neri, Zhumu Fu, Long Yu, Jian Wang, Yajie Yu","doi":"10.1111/mice.13464","DOIUrl":"https://doi.org/10.1111/mice.13464","url":null,"abstract":"Drop brackets (DB) and swivel clevises (SC) are critical components of railway catenary systems, playing a key role in maintaining cantilever stability. The condition of these components significantly impacts the safe operation of the catenary, necessitating periodic inspections to detect defects. This task is typically performed by onboard cameras using computer vision. However, traditional image processing methods often focus on shallow features, making it difficult to handle the interference from complex structures of components. While deep learning methods have strong capabilities in capturing semantic features, the lack of crack samples makes reliable crack identification challenging. Therefore, a joint approach for crack detection based on structural perception is proposed. The approach integrates three main components: object structure perception, stick structure perception, and crack defect detection. A multistream catenary components segmentation network (MCSnet) is employed to extract structural features of the DB and SC. Subsequently, an adaptive stick perception method (ASPM) is applied to identify potential crack candidates based on stick structure. The combined structural features enable effective detection of crack defects. Experimental results validate the effectiveness of the proposed approach.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"69 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structural performance prediction for structures and their components is a critical issue for ensuring the safety of civil engineering structures. Thus, enhancing the reliability of the prediction models with better generalization capability and quantifying the uncertainties of their predictions is significant. However, existing mechanism-driven and data-driven prediction models for reliable engineering applications incorporate complicated modifications on models and are sensitive to the precision of relevant prior knowledge. Focusing on these issues, a novel and concise data-driven approach, named “R2CU” (stands for transforming regression to classification with uncertainty-aware), is proposed to introduce the relative fuzzy prior knowledge into the data-driven prediction models. To enhance generalization capacity, the conventional regression task is transformed into a classification task based on the fuzzy prior knowledge and the experimental data. Then the aleatoric and epistemic uncertainty of the prediction is estimated to provide the confidence interval, which reflects the prediction's trustworthiness. A validation case study based on shear capacity prediction of reinforced concrete (RC) deep beams is carried out. The result proved that the model's generalization capability for extrapolating has been effectively enhanced with the proposed approach (the prediction precision was raised 80%). Meanwhile, the uncertainties within the model's prediction are rationally estimated, which made the proposed approach a practical alternative for structural performance prediction.
{"title":"Uncertainty-aware fuzzy knowledge embedding method for generalized structural performance prediction","authors":"Xiang-Yu Wang, Xin-Rui Ma, Shi-Zhi Chen","doi":"10.1111/mice.13457","DOIUrl":"https://doi.org/10.1111/mice.13457","url":null,"abstract":"Structural performance prediction for structures and their components is a critical issue for ensuring the safety of civil engineering structures. Thus, enhancing the reliability of the prediction models with better generalization capability and quantifying the uncertainties of their predictions is significant. However, existing mechanism-driven and data-driven prediction models for reliable engineering applications incorporate complicated modifications on models and are sensitive to the precision of relevant prior knowledge. Focusing on these issues, a novel and concise data-driven approach, named “R2CU” (stands for transforming regression to classification with uncertainty-aware), is proposed to introduce the relative <i>fuzzy prior knowledge</i> into the data-driven prediction models. To enhance generalization capacity, the conventional regression task is transformed into a classification task based on the fuzzy prior knowledge and the experimental data. Then the aleatoric and epistemic uncertainty of the prediction is estimated to provide the confidence interval, which reflects the prediction's trustworthiness. A validation case study based on shear capacity prediction of reinforced concrete (RC) deep beams is carried out. The result proved that the model's generalization capability for extrapolating has been effectively enhanced with the proposed approach (the prediction precision was raised 80%). Meanwhile, the uncertainties within the model's prediction are rationally estimated, which made the proposed approach a practical alternative for structural performance prediction.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"23 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yong Zhuang, Yuanjie Tang, Yingchen Qiu, Rengkui Liu
Short-term prediction of track degradation facilitates flexible and efficient maintenance, thereby meeting the railway system's escalating demands for track safety and smoothness. However, the track condition evolution presents challenges to accurate prediction, with diverse influential factors resulting in heterogeneous degradation patterns across space and time. In a short-term context, time series derived from historical records are length-limited, with sparse sampling points complicating feature identification. Actual activities, particularly minor repairs, lack strict periodicity, leading to irregular spans in continuous degradation curves, yielding nonuniform samples. This study leverages dynamic inspection and influential factors to propose an ensemble learning using the Transformer model. The outer framework employs unsupervised learning to group the sections based on specific time periods and track lengths. It assigns fuzzy logic categories to these groups to capture differentiated patterns and guides the division of samples into fuzzy subsets and assigns them to the learners corresponding to each cluster. The loosely coupled structure aids task decomposition and enhances local performance. The inner model refines the Transformer design for a new scenario, introducing a prediction objective transformation based on the interdependencies among multidimensional indicators to strengthen feature extraction. The prediction performance is evaluated using over 2 years of records from 560 km railway lines, offering insights for improving onsite track management.
{"title":"Short-term prediction of railway track degradation using ensemble deep learning","authors":"Yong Zhuang, Yuanjie Tang, Yingchen Qiu, Rengkui Liu","doi":"10.1111/mice.13462","DOIUrl":"10.1111/mice.13462","url":null,"abstract":"<p>Short-term prediction of track degradation facilitates flexible and efficient maintenance, thereby meeting the railway system's escalating demands for track safety and smoothness. However, the track condition evolution presents challenges to accurate prediction, with diverse influential factors resulting in heterogeneous degradation patterns across space and time. In a short-term context, time series derived from historical records are length-limited, with sparse sampling points complicating feature identification. Actual activities, particularly minor repairs, lack strict periodicity, leading to irregular spans in continuous degradation curves, yielding nonuniform samples. This study leverages dynamic inspection and influential factors to propose an ensemble learning using the Transformer model. The outer framework employs unsupervised learning to group the sections based on specific time periods and track lengths. It assigns fuzzy logic categories to these groups to capture differentiated patterns and guides the division of samples into fuzzy subsets and assigns them to the learners corresponding to each cluster. The loosely coupled structure aids task decomposition and enhances local performance. The inner model refines the Transformer design for a new scenario, introducing a prediction objective transformation based on the interdependencies among multidimensional indicators to strengthen feature extraction. The prediction performance is evaluated using over 2 years of records from 560 km railway lines, offering insights for improving onsite track management.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 10","pages":"1314-1343"},"PeriodicalIF":8.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yujie Ruan, Tao Huang, Cheng Yuan, Gang Zong, Qingzhao Kong
Current three-dimensional (3D) displacement measurement algorithms exhibit practical limitations, such as computational inefficiency, redundant point cloud data storage, reliance on preset targets, and restrictions to unidirectional measurements. This research aims to address computation efficiency and accuracy issues in binocular camera-based 3D structural displacement measurement by proposing a lightweight binocular vision-supported framework for structural 3D dynamic response monitoring. Through the optimization of sub-algorithms and code structures, this framework enhances both measurement accuracy and computational efficiency. The research incorporates a hybrid feature point processing algorithm and a lightweight tracking algorithm, which improve the accuracy of feature point recognition and tracking, enhance the adaptability and flexibility of the monitoring process, and increase tracking efficiency and overall system performance. These improvements make the framework more applicable to various civil engineering scenarios. Experimental validation on a full-scale three-story structure shows that the framework enables effective, target-free, 3D dynamic monitoring. Compared with reference displacement sensors, the framework achieves a relative root mean squared error of 14.6%, closely matching the accuracy of traditional methods that utilize accelerometers. The framework processes 1000 frames at 9.2 frames per second, offering a novel solution for contactless structural dynamic response monitoring in civil engineering applications, such as residential buildings and bridges, within a reasonable distance.
{"title":"A lightweight binocular vision-supported framework for 3D structural dynamic response monitoring","authors":"Yujie Ruan, Tao Huang, Cheng Yuan, Gang Zong, Qingzhao Kong","doi":"10.1111/mice.13452","DOIUrl":"https://doi.org/10.1111/mice.13452","url":null,"abstract":"Current three-dimensional (3D) displacement measurement algorithms exhibit practical limitations, such as computational inefficiency, redundant point cloud data storage, reliance on preset targets, and restrictions to unidirectional measurements. This research aims to address computation efficiency and accuracy issues in binocular camera-based 3D structural displacement measurement by proposing a lightweight binocular vision-supported framework for structural 3D dynamic response monitoring. Through the optimization of sub-algorithms and code structures, this framework enhances both measurement accuracy and computational efficiency. The research incorporates a hybrid feature point processing algorithm and a lightweight tracking algorithm, which improve the accuracy of feature point recognition and tracking, enhance the adaptability and flexibility of the monitoring process, and increase tracking efficiency and overall system performance. These improvements make the framework more applicable to various civil engineering scenarios. Experimental validation on a full-scale three-story structure shows that the framework enables effective, target-free, 3D dynamic monitoring. Compared with reference displacement sensors, the framework achieves a relative root mean squared error of 14.6%, closely matching the accuracy of traditional methods that utilize accelerometers. The framework processes 1000 frames at 9.2 frames per second, offering a novel solution for contactless structural dynamic response monitoring in civil engineering applications, such as residential buildings and bridges, within a reasonable distance.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the article Hidden structural information reconstruction and seismic response analysis of high-rise residential shear wall buildings with limited structural data by Chenyu Zhang et al., https://doi.org/10.1111/mice.13320.
{"title":"Cover Image, Volume 40, Issue 8","authors":"","doi":"10.1111/mice.13455","DOIUrl":"10.1111/mice.13455","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Hidden structural information reconstruction and seismic response analysis of high-rise residential shear wall buildings with limited structural data</i> by Chenyu Zhang et al., https://doi.org/10.1111/mice.13320.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 8","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the article An interactive cross-multi-feature fusion approach for salient object detection in crack segmentation by Jian Liu et al., https://doi.org/10.1111/mice.13437.
{"title":"Cover Image, Volume 40, Issue 8","authors":"","doi":"10.1111/mice.13456","DOIUrl":"10.1111/mice.13456","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>An interactive cross-multi-feature fusion approach for salient object detection in crack segmentation</i> by Jian Liu et al., https://doi.org/10.1111/mice.13437.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 8","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13456","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheng Hu, Jinjun Tang, Yaopeng Wang, Zhitao Li, Guowen Dai
The partitioning of a heterogeneously loaded road network into homogeneous, compact subregions is a fundamental prerequisite for the implementation of network-level traffic management and control based on the network macroscopic fundamental diagram. This study proposes a flexible road network partitioning framework that leverages the powerful feature extraction capabilities of self-supervised graph neural networks and employs a multi-objective optimization approach to balance regional homogeneity and compactness. A graph contrastive learning model is proposed to extract meaningful node embeddings that incorporate topology and attribute similarity information. Based on the learned node embeddings, the partition is determined by a parameter-free hierarchical clustering method and a subregion identification algorithm. Boundary tuning is then modeled as a bi-objective optimization problem to maximize regional homogeneity and compactness. A Pareto local search algorithm is developed to approximate the Pareto front. This study further demonstrates the extension of the proposed methods to scenarios with missing data. Finally, the methods are validated on real road networks with automatic license plate recognition data.
{"title":"A flexible road network partitioning framework for traffic management via graph contrastive learning and multi-objective optimization","authors":"Cheng Hu, Jinjun Tang, Yaopeng Wang, Zhitao Li, Guowen Dai","doi":"10.1111/mice.13454","DOIUrl":"https://doi.org/10.1111/mice.13454","url":null,"abstract":"The partitioning of a heterogeneously loaded road network into homogeneous, compact subregions is a fundamental prerequisite for the implementation of network-level traffic management and control based on the network macroscopic fundamental diagram. This study proposes a flexible road network partitioning framework that leverages the powerful feature extraction capabilities of self-supervised graph neural networks and employs a multi-objective optimization approach to balance regional homogeneity and compactness. A graph contrastive learning model is proposed to extract meaningful node embeddings that incorporate topology and attribute similarity information. Based on the learned node embeddings, the partition is determined by a parameter-free hierarchical clustering method and a subregion identification algorithm. Boundary tuning is then modeled as a bi-objective optimization problem to maximize regional homogeneity and compactness. A Pareto local search algorithm is developed to approximate the Pareto front. This study further demonstrates the extension of the proposed methods to scenarios with missing data. Finally, the methods are validated on real road networks with automatic license plate recognition data.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"53 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gang Pan, Chen Wang, Zhijie Sui, Shuai Guo, Yaozhi Lv, Honglie Li, Di Sun, Zixia Xia
The quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research on super-resolution for sewer images remains considerably unexplored. In response, this study leverages the inherent depth relationships present within QV images and introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet. It comprises two core components: a depth extraction module and a depth information matching module (DMM). DSRNet utilizes the adjacent frames of the low-resolution image as reference images and helps them recover texture information based on the correlation. By combining these modules, the integration of depth priors significantly enhances both visual quality and performance benchmarks. Besides, in pursuit of computational efficiency and compactness, a super-resolution knowledge distillation model based on an attention mechanism is introduced. This mechanism facilitates the acquisition of feature similarity between a more complex teacher model and a streamlined student model, with the latter being a lightweight version of DSRNet. Experimental results demonstrate that DSRNet significantly improves peak signal-to-noise ratio (PSNR) and and Structural Similarity index (SSIM) compared with other methods. This study also conducts experiments on sewer defect semantic segmentation, object detection, and classification on the Pipe data set and Sewer-ML data set. Experiments show that the method can improve the performance of low-resolution sewer images in these tasks.
{"title":"Sewer image super-resolution with depth priors and its lightweight network","authors":"Gang Pan, Chen Wang, Zhijie Sui, Shuai Guo, Yaozhi Lv, Honglie Li, Di Sun, Zixia Xia","doi":"10.1111/mice.13453","DOIUrl":"https://doi.org/10.1111/mice.13453","url":null,"abstract":"The quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research on super-resolution for sewer images remains considerably unexplored. In response, this study leverages the inherent depth relationships present within QV images and introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet. It comprises two core components: a depth extraction module and a depth information matching module (DMM). DSRNet utilizes the adjacent frames of the low-resolution image as reference images and helps them recover texture information based on the correlation. By combining these modules, the integration of depth priors significantly enhances both visual quality and performance benchmarks. Besides, in pursuit of computational efficiency and compactness, a super-resolution knowledge distillation model based on an attention mechanism is introduced. This mechanism facilitates the acquisition of feature similarity between a more complex teacher model and a streamlined student model, with the latter being a lightweight version of DSRNet. Experimental results demonstrate that DSRNet significantly improves peak signal-to-noise ratio (PSNR) and and Structural Similarity index (SSIM) compared with other methods. This study also conducts experiments on sewer defect semantic segmentation, object detection, and classification on the Pipe data set and Sewer-ML data set. Experiments show that the method can improve the performance of low-resolution sewer images in these tasks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"48 9 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vincent J. L. Gan, Difeng Hu, Yushuo Wang, Ruoming Zhai
Advancements in automated 3D scene reconstruction are essential for accurately capturing and documenting the current state of buildings and infrastructure. Traditional 3D reconstruction relies on laser scanning to obtain as-built conditions, but this process is often labor-intensive and time-consuming. This study introduces an optimization algorithm incorporating methods for viewpoint generation, occlusion detection and culling, and robot-moving trajectory identification. Additionally, the research investigates 3D reconstruction methods, comparing coupled and decoupled approaches to identify the most practical configuration for robotic scanning. Automation strategies for collision avoidance in human-centric environments are also explored, with adaptive control methods tested and validated for efficient point cloud data capture in indoor environments. This research advances the state-of-the-art in robotic scanning by providing a more precise and adaptive framework for 3D scene reconstruction. The results demonstrate the effectiveness of the proposed method in achieving high scan completeness and sufficient density in point cloud data, offering solutions for efficient robotic scanning.
{"title":"Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor","authors":"Vincent J. L. Gan, Difeng Hu, Yushuo Wang, Ruoming Zhai","doi":"10.1111/mice.13450","DOIUrl":"https://doi.org/10.1111/mice.13450","url":null,"abstract":"Advancements in automated 3D scene reconstruction are essential for accurately capturing and documenting the current state of buildings and infrastructure. Traditional 3D reconstruction relies on laser scanning to obtain as-built conditions, but this process is often labor-intensive and time-consuming. This study introduces an optimization algorithm incorporating methods for viewpoint generation, occlusion detection and culling, and robot-moving trajectory identification. Additionally, the research investigates 3D reconstruction methods, comparing coupled and decoupled approaches to identify the most practical configuration for robotic scanning. Automation strategies for collision avoidance in human-centric environments are also explored, with adaptive control methods tested and validated for efficient point cloud data capture in indoor environments. This research advances the state-of-the-art in robotic scanning by providing a more precise and adaptive framework for 3D scene reconstruction. The results demonstrate the effectiveness of the proposed method in achieving high scan completeness and sufficient density in point cloud data, offering solutions for efficient robotic scanning.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"18 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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