Extraction Of Geometric Parameters Research Articles

Efficient Detection of Apparent Defects in Subway Tunnel Linings Based on Deep Learning Methods

High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model’s backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model’s head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels.

UAV-based individual plant detection and geometric parameter extraction in vineyards.

Accurately characterizing vineyard parameters is crucial for precise vineyard management and breeding purposes. Various macroscopic vineyard parameters are required to make informed management decisions, such as pesticide application, defoliation strategies, and determining optimal sugar content in each berry by assessing biomass. In this paper, we present a novel approach that utilizes point cloud data to detect trunk positions and extract macroscopic vineyard characteristics, including plant height, canopy width, and canopy volume. Our approach relies solely on geometric features and is compatible with different training systems and data collected using various 3D sensors. To evaluate the effectiveness and robustness of our proposed approach, we conducted extensive experiments on multiple grapevine rows trained in two different systems. Our method provides more comprehensive canopy characteristics than traditional manual measurements, which are not representative throughout the row. The experimental results demonstrate the accuracy and efficiency of our method in extracting vital macroscopic vineyard characteristics, providing valuable insights for yield monitoring, grape quality optimization, and strategic interventions to enhance vineyard productivity and sustainability.

A Data and Knowledge-driven framework of the intelligent process design for special-shaped features of complex aviation parts

While the safety and reliability of aviation equipment are directly determined by the performance of complex aviation parts, these kinds of components still contain lots of special-shaped features, which refer to the machining features that need to be completed by unique process and non-standard cutting tools. In addition, the traditional process design of special-shaped features of complex aviation parts (SFCAP) heavily relies on manual experience, which leads to low machining efficiency and unstable machining quality. To solve this problem, this paper takes SFCAP as the study object and proposes a framework of intelligent process design, which can effectively guarantee the processing quality to support the performance of complex aviation equipment by realizing active adaptive adjustment, rapid iteration, and upgrading of process planning based on the machining data and relative knowledge. Four key-enabling technologies are set forth to support the design, which include the special-shaped feature-process knowledge mapping model, 3D feature recognition and geometric parameter extraction, knowledge uncertainty-based evaluation, and digital twin-based verification and optimization. What's more, the benefits and challenges of the design are analyzed, and the contribution and deficiencies are given at the end of this paper.

The automatic interpretation of structural plane parameters in borehole camera images from drilling engineering

Digital panoramic borehole camera technology has been widely employed in actual projects, and a large number of high-accuracy borehole camera images have been obtained. Borehole camera images accurately record the geological information, especially the feature parameters of structure plane. However, since the acquisition of these features is usually done by hand, the workload is large and the results can be affected by human error. To solve this problem, this paper presents an automatic interpretation method of structure plane parameters in borehole camera image. In this method, image pixel gray, gradient values and projection method are employed to distinguish the occurrence region of structural planes. Then, standard sine function matching method is employed to search the structure plane in the region. Lastly, the optimal sine curve is screened out and adopted as the feature curve of structure plane. Related parameters of sine curve are analyzed and converted into the parameters of structure plane, such as the central position, orientation, dip angle and fracture width of structural planes. This method can automatically identify structure planes in the image continuously and quickly, and obtain the corresponding structural parameters. The method is stable and reliable, and greatly improves the working efficiency. It can perform the automatic interpretation of structure planes and extraction of geometric parameters, and provide an effective and consistent solution for drilling information acquisition and borehole camera image signal processing.

An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-sections

The characterisation the vertical profiles and cross-sections of roads is important for the verification of proper construction and road safety assessment. The goal of this paper is the extraction of geometric parameters through the automatic processing of mobile LiDAR system (MLS) point clouds. Massive and complex datasets provided by the MLS are processed using a hierarchical strategy that includes segmentation, principal component analysis (PCA)-based orthogonal regression, filtering and parameter extraction procedures. Best-fit geometric parameters act as a vertical road model for both linear parameters (slope and vertical curves) and cross-sections (superelevations). The proposed automatic processing approach gives satisfactory results for the analysed scenario.

Using Stereo Satellite Imagery for Topographic and Transportation Applications: An Accuracy Assessment

The launch of the Very High Resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of satellite stereo imaging for many applications. The objective of this paper is to evaluate the level of accuracy that can be achieved by using stereo satellite images for different applications involving significantly different types of terrain. Three mathematical models for satellite sensor modeling are used: Rational Function Model (RFM), 3D polynomial model, and 3D affine model. Three stereo pairs of image datasets are tested from different satellites for different areas: (a) Indian Remote Sensing (IRS)-1D stereo images for topographic mapping and digital terrain elevation modeling for an area in Egypt; (b) IKONOS stereo images for highway alignments extraction in Toronto, Canada; and (c) IKONOS stereo images for topographic mapping and geometric parameter extraction for highway alignments in Hong Kong, China. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from conventional techniques, including Global Positioning System, field measurements, and aerial photogrammetry. The accuracy of the extracted features was found to be within a pixel-level. The results of this paper should be of interest to professionals from different disciplines exploring the use and accuracy of satellite stereo images for topographic and transportation applications.

Improved model-based infrared reflectrometry for measuring deep trench structures

Model-based infrared reflectrometry (MBIR) has been introduced recently for characterization of high-aspect-ratio deep trench structures in microelectronics. The success of this technique relies heavily on accurate modeling of trench structures and fast extraction of trench parameters. In this paper, we propose a modeling method named corrected effective medium approximation (CEMA) for accurate and fast reflectivity calculation of deep trench structures. We also develop a method combining an artificial neural network (ANN) and a Levenberg-Marquardt (LM) algorithm for robust and fast extraction of geometric parameters from the measured reflectance spectrum. The simulation and experimental work conducted on typical deep trench structures has verified the proposed methods and demonstrated that the improved MBIR metrology achieves highly accurate measurement results as well as fast computation speed.

Layout-Aware Compact Model of MOSFET Characteristics Variations Induced by STI Stress

A compact model is proposed for accurately incorporating effects of STI (shallow trench isolation) stress into post-layout simulation by making layout-dependent corrections to SPICE model parameters. The model takes in-plane (longitudinal and transverse) and normal components of the layout-dependent stress into account, and model formulas are devised from physical considerations. Not only can the model handle the shape of the active-area of any MOSFET conforming to design rules, but also considers distances to neighboring active-areas. Extraction of geometrical parameters from the layout can be performed by standard LVS (layout versus schematic) tools, and the corrections can subsequently be back-annotated into the netlist. The paper spells out the complete formulation by presenting expressions for the mobility and the threshold voltage explicitly by way of example. The model is amply validated by comparisons with experimental data from 90 nm- and 65 nm-CMOS technologies having the channel orientations of, respectively, (110) and (100), both on a (100) surface. The worst-case standard errors turn out to be as small as 1.7% for the saturation current and 8 mV for the threshold voltage, as opposed to ∼20% and ∼50 mV without the model. Since device characteristics variations due to STI stress constitute a significant part of what have conventionally been treated as random variations, use of the proposed model could enable one to greatly narrow the guardbands required to guarantee a desired yield, thereby facilitating design closure.

Propeller Geometric Parameter Extraction From Inspection Data Clouds

Methods and conventions for propeller geometric parameter extraction from inspection data clouds are presented. Steps in the extraction process include constructing a representation of the inspected propeller from inspection data clouds, constructing sections through the representation, mapping the sections from three to two dimensions, and computing propeller parameters for the sections.

Arrays of ungated GaAs field emitters fabricated by wet or dry etching

Dry and wet etching methods are used to fabricate arrays of ungated GaAs field emitters. Comparisons were made among tips, wedges, pillars and walls. Effects of the etching technique and subsequent deoxidation procedure on the GaAs surface are evaluated in terms of emitter geometry, uniformity, heating and integration. Moreover, the current emission capability of field emitter arrays is investigated. In particular, the influence of a thin surface oxide layer on the emission stability is observed. An extraction of geometrical parameters is attempted on the basis of a statistical examination of the emission characteristics and the validity of this approach is discussed.

On the collective contribution to the nuclear optical potential

The possible importance of collective effects on the nuclear optical potential, and upon the extraction of geometrical parameters of the matter distribution is discussed. The formal problem is delimited, and an empirical study is made for some cases.