Application Research of ship overload identification algorithm based on lidar point cloud

Zhang, Wenkai; Wu, Yingrui; Tian, Xin; Bao, Wenhan; Yu, Tianqi; Yang, Jianfeng

doi:10.1109/iceemt56362.2022.9862748

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Point cloud data directly provide the shape, size, and spatial position of objects in threedimensional space. Zhang et al [16] utilize 3D point cloud data to identify overloaded ships. Lu et al [17] conduct the precise tracking of key points on ships to facilitate intelligent docking.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Maritime Scenes under Adverse Weather Conditions

Zhang,

Wang,

Meng

et al. 2024

Remote Sensing

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Point cloud-based detection focuses on land traffic, rarely marine, facing issues with ships: it struggles in bad weather due to reliance on adverse weather data and fails to detect ships effectively due to overlooking size and appearance differences. Addressing the above challenges, our work introduces point cloud data of marine scenarios under realistically simulated adverse weather conditions and a dedicated Ship Detector tailored for marine environments. To adapt to various maritime weather conditions, we simulate realistic rain and fog in collected marine scene point cloud data. Additionally, addressing the issue of losing geometric and height information during feature extraction for large objects, we propose a Ship Detector. It employs a dual-branch sparse convolution layer for extracting multi-scale 3D feature maps, effectively minimizing height information loss. Additionally, a multi-scale 2D convolution module is utilized, which encodes and decodes feature maps and directly employs 3D feature maps for target prediction. To reduce dependency on existing data and enhance model robustness, our training dataset includes simulated point cloud data representing adverse weather conditions. In maritime point cloud ship detection, our Ship Detector, compared to adjusted small object detectors, demonstrates the best performance.

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Section: Introductionmentioning

confidence: 99%

Ship Detection in Maritime Scenes under Adverse Weather Conditions

Zhang,

Wang,

Meng

et al. 2024

Remote Sensing

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“…Recently, advanced point cloud technology has been applied in the ship engineering field. For instance, in the construction quality analysis of hull blocks [1], steel parts reconstruction [2], ship overload identification [3], ship berthing angle estimation [4], and 3D target detection for ship navigation [5]. However, the dense point cloud acquired by terrestrial laser scans (TLS) technology in the ship engineering field (Figure 1 shows two instances) presents an obstacle to point cloud processing technologies.…”

Section: Introductionmentioning

confidence: 99%

A Pre-Procession Module for Point-Based Deep Learning in Dense Point Clouds in the Ship Engineering Field

Huo,

Liu,

Wang

et al. 2023

JMSE

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Recently, point cloud technology has been applied in the ship engineering field. However, the dense point cloud acquired by terrestrial laser scanning (TLS) technology in ship engineering applications brings an obstacle to some powerful and advanced point-based deep learning point cloud processing methods. This paper presents a deep learning pre-procession module to ensure the feasibility of processing dense point clouds on commonly available computer devices. The pre-procession module is designed according to the traditional point cloud processing methods and the PointNet++ paradigm, and is evaluated on two ship structure datasets and two popular point cloud datasets. Experimental results illustrate that (i) the proposed module improves the performance of point-based deep learning semantic segmentation networks, and (ii) the proposed module empowers the existing point-based deep learning networks with the capability to process dense input point clouds. The proposed module may provide a useful semantic segmentation tool for realistic dense point clouds in various industrial applications.

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An investigation of geometric feature recognition in 3D ship data

Guo,

Du,

2024

International Journal of Naval Architecture and Ocean Engineeri

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Application Research of ship overload identification algorithm based on lidar point cloud

Cited by 3 publications

References 1 publication

Ship Detection in Maritime Scenes under Adverse Weather Conditions

Ship Detection in Maritime Scenes under Adverse Weather Conditions

A Pre-Procession Module for Point-Based Deep Learning in Dense Point Clouds in the Ship Engineering Field

An investigation of geometric feature recognition in 3D ship data

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