Contour detection and salient feature line regularization for printed circuit board in point clouds based on geometric primitives

Li, Xurui; Liu, Guangshuai; Sun, Shanhui; Bai, Chun

doi:10.1016/j.measurement.2021.109978

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…The point clouds of bonding wire, denoted as

S\left({p}_{i}\right)

, p i is each point could in the space. Use the k‐d tree [30] to query the n points near point p i , denoted as

N\left({p}_{i}\right)

. Calculate the normal vector n i of p i .…”

Section: Methodsmentioning

confidence: 99%

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

Qiao

et al. 2023

CAAI Trans on Intel Tech

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Non‐destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image‐processing‐based methods do not provide a detailed evaluation of the three‐dimensional defects of the bonding wire. Therefore, a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision, which can achieve non‐destructive detection of bonding wire defects is proposed. The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires. Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface, a point cloud segmentation method based on spatial surface feature detection (SFD) was proposed. SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process. Furthermore, in the defect detection process, a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires. The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires. The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires, and the average accuracy of defect recognition is 96.47%, which meets the production requirements of bonding wire defect detection.

show abstract

“…The point clouds of bonding wire, denoted as

S\left({p}_{i}\right)

, p i is each point could in the space. Use the k‐d tree [30] to query the n points near point p i , denoted as

N\left({p}_{i}\right)

. Calculate the normal vector n i of p i .…”

Section: Methodsmentioning

confidence: 99%

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

Qiao

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Significant elements 𝒮 refer to objects that have advantages in size, visual information, and contextual distinguishability with the given scene such as boundaries in PCBs [73], holes of aircraft skins [74], markers on scenarios [75], and designated contours in parts [76]. Determining the feature space means that significant elements are expected to be transformed into a domain independent public space to build correspondence with a given pattern by, for example, encoding elements into 2D, 3D [77], or pose [43] geometric space to perform matching with a given pattern (e.g., pointset, 3D model) [55].…”

Section: Significant Elements and Feature Spacementioning

confidence: 99%

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

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The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (, 153.584 mm) to (, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication.

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RetinaNet-vline: a flexible small target detection algorithm for efficient aggregation of information

Liu

Zhang

2023

Cluster Comput

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Contour detection and salient feature line regularization for printed circuit board in point clouds based on geometric primitives

Cited by 9 publications

References 20 publications

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

RetinaNet-vline: a flexible small target detection algorithm for efficient aggregation of information

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