Condition assessment of ship structure using robot assisted 3D-reconstruction

Shah, Faisal Mehmood; Gaggero, Tomaso; Gaiotti, Marco; Rizzo, Cesare Mario

doi:10.1080/09377255.2021.1872219

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…First, the luminance component v in the channel of neurogenic intestine was extracted, and the illumination component was extracted by multiscale Gaussian blur. Second, the 2D gamma function is used to adjust the brightness component of the original image, and the highlight image is corrected on the premise of effectively retaining the effective information of the original image [17,18]. By recovering the global topological structure of the three-dimensional curve segment, the subspace three-dimensional curve segment of the binocular system was data concatenated to find the optimal path to reconstruct the complete three-dimensional model of the catheter centerline.…”

Section: D Image De-highlighting Algorithmmentioning

confidence: 99%

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Shen

Huang

Zhang

et al. 2022

Scientific Programming

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Neurogenic intestinal care is currently a major research topic in the medical field. In order to improve the accuracy of neurogenic intestinal care and simplify the process of neurogenic intestinal care, this paper adopts a three-dimensional analysis theory to extract the key indicators in neurogenic intestinal care. Through the analysis and calculation of the test data, the calculation results of neurogenic intestinal care under the effect of three-dimensional reconstruction technology were obtained. Through the analysis of the results, the calculation law under the action of the optimization model can be obtained. Based on the three-dimensional reconstruction theory, the contents of neurogenic intestinal care were analyzed by the three-dimensional reconstruction de-highlighting algorithm. The three-dimensional curve was used to screen the key indicators of neurogenic intestinal care, so as to obtain the optimized heavy weight model, and the three-dimensional reconstruction model can analyze the indicators of neurogenic intestinal care. It can be seen from relevant studies that different indicators have different trends in their range of change in the curve projection diagram of the 3D curve screening method. Among them, the curve shadow and curve point have obvious downward changes, while the curve deviation has U-shaped changes. The range of curve weights is relatively large. The variation rule between different data in the 3D curve segment can be obtained through the cost function. Among them, the cost function index and the cost model parameter have the same change trend, and the overall fluctuation range is relatively small through the space index. In the calculation results of neurogenic intestinal care, different factors will have a great influence on the calculation results. Neurons and intestinal peristalsis have the same change trend, while the linear characteristics of nerve endings are relatively obvious, and the fluctuation characteristics of gastrointestinal digestion are good. The health standard decreases linearly with the increase of the sample, and the dynamic reconstruction has a positive effect on the data. This study can provide research ideas for the analysis of neurogenic gut.

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Section: D Image De-highlighting Algorithmmentioning

confidence: 99%

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Shen

Huang

Zhang

et al. 2022

Scientific Programming

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“…The acquired data information is then analyzed and processed and, finally, the theory of 3D reconstruction is used to reconstruct the contour information for the object surface in the real environment [2][3][4][5][6][7][8][9]. Vision-based 3D reconstruction technology has been widely used in the fields of unmanned vehicles [10], virtual reality [11], 3D printing [12], and engineering surveys [13].…”

Section: Introductionmentioning

confidence: 99%

Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

et al. 2023

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At present, Chinese 3D reconstruction solutions using stereo cameras mainly face known, indoor, structured scenes; for the reconstruction of unstructured, larger-scale scenes with a large variety of texture information of different intensities, there are certain difficulties in ensuring accuracy and real-time processing. For the above problems, we propose a scene reconstruction method using stereo vision. Firstly, considering the influence of outdoor lighting and weather on the captured 2D images, the optimized SAD-FAST feature detection algorithm and stereo-matching strategy were employed in the stereo-matching stage to improve the overall efficiency and matching quality at this stage. Then, a homogenized feature extraction algorithm with gradient value decreasing step by step (GVDS) was used in the depth value calculation to ensure a sufficient number of feature points for strong texture information while extracting features from weak-texture areas, which greatly improved the quality and speed of unstructured scene reconstruction. We conducted experiments to validate the proposed method, and the results showed the feasibility of the proposed method and its high practical value.

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“…Second, we select an optimal AI model that can simultaneously perform ship classification and structure recognition using visual sensors and repeatedly learned it using the MTMnet dataset. We compared and analyzed each model using the Detectron2 platform to apply diverse AI models [ 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Lim

Kim

Hong

et al. 2022

Sensors

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The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.

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Condition assessment of ship structure using robot assisted 3D-reconstruction

Cited by 6 publications

References 45 publications

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

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