A fast recognition algorithm of ship hatch in bulk cargo terminal based on point cloud contour extraction

Li, Yuan; Li, Zhan; Yang, Yipeng; Liang, Zhao; Liu, Yang

doi:10.1177/00202940221090967

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…(2) Different from the monogeneity of feature point information in literature, [33][34][35][36][37] the algorithm determines feature contour points [50][51][52]…”

Section: Contributionmentioning

confidence: 99%

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Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Zhao,

Zhang,

et al. 2023

Measurement and Control

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For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.

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“…(2) Different from the monogeneity of feature point information in literature, [33][34][35][36][37] the algorithm determines feature contour points [50][51][52]…”

Section: Contributionmentioning

confidence: 99%

“…(2) Different from the monogeneity of feature point information in literature, 33–37 the algorithm determines feature contour points 50–52 by calculating edge point clouds, and carries out rough registration of contour point clouds with edge information.…”

Section: Introductionmentioning

confidence: 99%

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Zhao,

Zhang,

et al. 2023

Measurement and Control

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“…An algorithm converts the cloud points data into a 2D plane. Finally, other algorithms obtain the cargo hold's x and y coordinates and recreate the ship's deck and hatch edges [1], [7].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, building an automated bulk operations load system is based and premised on the recognition of ship hatches. In this context, detecting the hatch rapidly and accurately is still a problem that needs an urgent solution in the current port production scenario [1]. Weather conditions, such as rainfall, significantly impact bulk operation loading systems.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Detecting Ship Hatch Closing Moment: A Case Study Using YOLO Family

Pimentel,

Reis,

Gomes Jr.

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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This paper presents a neural network-based computer vision approach for detecting ship cargo-hold hatch closure. The investigation is relevant since weather conditions, especially rainfall, cause damage to cargo such as sodium sulfate, sugar, corn, corn bran, and potassium chloride, among others. Registering when the cargo hold hatch is closed could prevent damage to the cargo, avoiding prejudice to transportation companies. Our proposal uses YOLO framework vision detection as an economical alternative to the current state-of-the-art for detecting ship hatch closing with expensive and complex solutions. This investigation presents an experiment in a tailored dataset, and results are applied to real-time video detection that validates a stable and accurate solution to the problem of ship hatch detection. Results have shown that even though regular YOLO v4 reaches better metrics, with an accuracy of 91.55%, Fast YOLO v4 is better for real-time detection but with a penalty of lower accuracy.

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3D surface change detection of slope using 3D point cloud based on construction of double surface

Ma,

Zhao,

et al. 2025

Measurement

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A fast recognition algorithm of ship hatch in bulk cargo terminal based on point cloud contour extraction

Cited by 9 publications

References 8 publications

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction

Convolutional Neural Networks for Detecting Ship Hatch Closing Moment: A Case Study Using YOLO Family

3D surface change detection of slope using 3D point cloud based on construction of double surface

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