Surface Defect Detection of Cabbage Based on Curvature Features of 3D Point Cloud

Gu, Jun; Zhang, Yawei; Yin, Yong; Deng, Junwen; Zhang, Bin

doi:10.3389/fpls.2022.942040

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…However, this method requires that the intersection lines be visible. Region-of-interest (ROI) approaches are well-suited for extracting relevant point clouds [ 32 , 33 ]. When planar surfaces within a scene lack notable distinctions or exhibit visual similarity, a priori (ROI) information can be employed to segment and differentiate these planes [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Localization on a-priori information of plane extraction

Zhao

Misyurin

et al. 2023

PLoS ONE

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Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall’s surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

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Section: Introductionmentioning

confidence: 99%

Localization on a-priori information of plane extraction

Zhao

Misyurin

et al. 2023

PLoS ONE

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“…With the development of local feature descriptors [21,22], most researchers used curvature [23,24], offset [25], and normal vectors [26,27] to characterize local features and thus detect defect regions. However, various types of defect structures exist, which leads to the need to set a large number of parameters when detecting different types of defects.…”

Section: Introductionmentioning

confidence: 99%

Defect segmentation with local embedding in industrial 3D point clouds based on transformer

Jing,

Wang

2023

Meas. Sci. Technol.

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Three-dimensional (3D) defect detection provides an effective method for improving industrial production efficiency. However, the 3D dataset is scarce, which is valuable for the industrial production field. This study proposes a new approach for detecting defect point clouds, which can provide an end-to-end 3D defect detection model. A self-attention mechanism is used to enrich the semantic relationships between local neighborhood features and global features based on the connection between them. Through adding multi-channel features, the rich structural features of the target point cloud are obtained, and the defect areas are accurately segmented to finally complete the 3D point cloud defect detection task. Furthermore, the multi-feature fusion in the model makes the segmented defect regions closer to the ground truth. Our method outperforms four state-of-the-art point cloud segmentation methods in terms of both segmentation region accuracy and defect detection point cloud accuracy. In the field of 3D defect detection, it provides an effective method to detect 3D information of industrial products.

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“…Point clouds, one type of common 3D data, can accurately depict the physical world and represent 3D shapes with a more concise data structure than image data. Point cloud-based defect detection techniques have been widely used in various industries, such as aircraft manufacturing [6], rail manufacturing [7], PCB [8], heritage restoration [9], and agriculture and forestry industries [10]. Object defect detection based on 3D point clouds usually includes methods based on standard model comparison [11][12][13][14] and methods based on point cloud geometric features [6,[15][16][17].…”

Section: Introductionmentioning

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.

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Surface Defect Detection of Cabbage Based on Curvature Features of 3D Point Cloud

Cited by 7 publications

References 25 publications

Localization on a-priori information of plane extraction

Localization on a-priori information of plane extraction

Defect segmentation with local embedding in industrial 3D point clouds based on transformer

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

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