Computer Vision Techniques in Construction: A Critical Review

Xu, Shuyuan; Wang, Jun; Shou, Wenchi; Ngo, Tuan; Sadick, Abdul-Manan; Wang, Xiangyu

doi:10.1007/s11831-020-09504-3

Cited by 261 publications

(121 citation statements)

References 120 publications

Supporting

Mentioning

116

Contrasting

Unclassified

Order By: Relevance

“…Several kinds of natural hazards related environment problems has been solved by various research groups for sustainable management and utilization of natural resources (Pradhan and Kim, 2017;Jiang et al, 2018;Tsai et al, 2019;Wang et al, 2020;Xu et al, 2021) with the help of remote sensing (RS) technology (Han et al, 2019;Hu et al, 2020;Zhang et al, 2020c) and geographical information system (GIS) tool (Zuo et al, 2015;Xu et al, 2018;Zhu et al, 2019;Yang et al, 2020b) and widespread progress in the computational facilities (Chao et al, 2018;Zhang et al, 2018Zhang et al, , 2020bCao et al, 2020;Xu et al, 2020;Feng et al, 2020). A noteworthy support from the combination of RS and GIS technology gives efficiently solution in the several types of natural hazards related problems (Yang et al, 2018(Yang et al, , 2020aSun et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

View full text Add to dashboard Cite

The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

show abstract

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Besides, this review on COVID19 detection systems shows that most of the existing deep learning-based systems have used deep CNN-based networks [45][46][47][48][49]; thereby, we propose to employ the ability of deep CNN as a COVID 19 detector.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Khishe²,

Mohammadi

et al. 2021

Soft Comput

View full text Add to dashboard Cite

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters’ stochastic tuning of ELM’s supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine–cosine algorithm was utilized to tune the ELM’s parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network’s training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.

show abstract

“…Author details 1 College of Information Engineering, Chaohu Univeristy, Chaohu 238000, China. 2 First Affiliated Branch, Shenyang Radio and TV University Shenyang, Liaoning 110003, China.…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

“…limited computing power, long-term continuous low latency and high quality of service requirements [2]. As a technology usually integrated with IoT, MEC can supply services and cloud computing functions needed by wireless users nearby and has the potential to provide real-time, low-latency services to the underlying network [3].…”

mentioning

confidence: 99%

Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

Cheng

Guan²

2021

J Wireless Com Network

View full text Add to dashboard Cite

As a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

show abstract

Computer Vision Techniques in Construction: A Critical Review

Cited by 261 publications

References 120 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

Contact Info

Product

Resources

About