A Comparison of the Performance of 2D and 3D Convolutional Neural Networks for Subsea Survey Video Classification

Stamoulakatos, Anastasios; Cardona, Javier; Michie, Craig; Andonović, Ivan; Lazaridis, Pavlos I.; Bellekens, Xavier; Atkinson, Robert; Hossain, Moinul; Tachtatzis, Christos

doi:10.23919/oceans44145.2021.9706125

Cited by 7 publications

(2 citation statements)

References 50 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, another study also gave positive results. For example, 2D convolutional neural networks achieved an F1-Score of 90% [40]. Xie et al [41] used acoustic signals to train a time-frequency distribution map-based convolutional neural network model for underwater pipeline leakage detection, showing 95.5% of accuracy.…”

Section: Classification Resultsmentioning

confidence: 99%

Application of EfficientNet and YOLOv5 Model in Submarine Pipeline Inspection and a New Decision-Making System

Li,

Han

et al. 2023

Water

View full text Add to dashboard Cite

Submarine pipelines are the main means of transporting oil and gas produced offshore. The present work proposed a deep learning technology to identify damage caused by characteristic events and abnormal events using pipeline images collected by remotely operated vehicles (ROVs). The EfficientNet and You Only Look Once (YOLO) models were used in this study to classify images and detect events. The results show that the EfficentNet model achieved the highest classification accuracy at 93.57 percent, along with a recall rate of 88.57 percent. The combining of the EfficentNet and YOLOv5 models achieved a higher accuracy of detecting submarine pipeline events and outperformed any other methods. A new decision-making system that integrates the operation and maintenance of the model is proposed and a convenient operation is realized, which provides a new construction method for the rapid inspection of submarine pipelines. Overall, the results of this study show that images acquired via ROVs can be applied to deep learning models to examine submarine pipeline events. The deep learning model is at the core of establishing an effective decision support system for submarine pipeline inspection and the overall application framework lays the foundation for practical application.

show abstract

Section: Classification Resultsmentioning

confidence: 99%

Application of EfficientNet and YOLOv5 Model in Submarine Pipeline Inspection and a New Decision-Making System

Li,

Han

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…This ability is important for understating body postures and fine-grained motion details given the dynamic HrT environments, as highlighted in the recent literature [33]. Moreover, 2DCNNs offer better computational efficiency compared to 3DCNNs [50], which is particularly important for real-time or resource-constrained applications.…”

Section: Action Recognition Model Configurations Based On the Inhard ...mentioning

confidence: 99%

Action Recognition for Human–Robot Teaming: Exploring Mutual Performance Monitoring Possibilities

Mehak,

Kelleher,

Guilfoyle

et al. 2024

Machines

View full text Add to dashboard Cite

Human–robot teaming (HrT) is being adopted in an increasing range of industries and work environments. Effective HrT relies on the success of complex and dynamic human–robot interaction. Although it may be optimal for robots to possess all the social and emotional skills to function as productive team members, certain cognitive capabilities can enable them to develop attitude-based competencies for optimizing teams. Despite the extensive research into the human–human team structure, the domain of HrT research remains relatively limited. In this sense, incorporating established human–human teaming (HhT) elements may prove practical. One key element is mutual performance monitoring (MPM), which involves the reciprocal observation and active anticipation of team members’ actions within the team setting, fostering enhanced team coordination and communication. By adopting this concept, this study uses ML-based visual action recognition as a potential tool for developing an effective way to monitor the human component in HrT. This study utilizes a data modeling approach on an existing dataset, the “Industrial Human Action Recognition Dataset” (InHARD), curated specifically for human action recognition assembly tasks in industrial environments involving human–robot collaborations. This paper presents the results of this modeling approach in analyzing the dataset to implement a theoretical concept that can be a first step toward enabling the system to adapt dynamically. The outcomes emphasize the significance of implementing state-of-the-art team concepts by integrating modern technologies and assessing the possibility of advancing HrT in this direction.

show abstract

A Three-Stage 2D–3D Convolutional Network Ensemble for Segmenting Malignant Brain Tumors on MRI Images

2023

View full text Add to dashboard Cite

A Comparison of the Performance of 2D and 3D Convolutional Neural Networks for Subsea Survey Video Classification

Cited by 7 publications

References 50 publications

Application of EfficientNet and YOLOv5 Model in Submarine Pipeline Inspection and a New Decision-Making System

Application of EfficientNet and YOLOv5 Model in Submarine Pipeline Inspection and a New Decision-Making System

Action Recognition for Human–Robot Teaming: Exploring Mutual Performance Monitoring Possibilities

A Three-Stage 2D–3D Convolutional Network Ensemble for Segmenting Malignant Brain Tumors on MRI Images

Contact Info

Product

Resources

About