A corner detection method based on adaptive multi-directional anisotropic diffusion

Bao, Junmin; Jing, Junfeng; Zhang, Weichuan; Liu, Chao; Gao, Tengfei

doi:10.1007/s11042-022-12666-w

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…During tube yarn hairiness defect detection stage, the feature points in the images acquired by the camera are detected using the method that we previous researched in Ref. 26. Since the yarn image is captured with a black background, feature points are almost distributed in the regions where the black background and the white yarn meet, i.e.…”

Section: Tube Yarn Hairiness Defect Detectionmentioning

confidence: 99%

A defect detection system of glass tube yarn based on machine vision

Bao

Jing

Xie

2023

Journal of Industrial Textiles

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Tube yarn is also called glass fiber spun yarn. Due to the excellent properties of glass fiber, various industrial products based on glass fiber are used in a variety of industries. As the most obvious factor affecting the quality of products, quality detection of glass fiber yarns is very important for companies. Due to traditional defect detection relies on the experience and subjective factors of workers, which makes many different views on the same defect. Some traditional methods limit the solution to specific types of defects and do not accurately detect various defects. In this paper, we propose a comprehensive defect detection system of tube yarn via combining machine vision and deep learning methods. In whole system, we inspect the weight through a weight sensor firstly. Then, we propose a multi-scale cross-fusion attention module to improve the MobileNetV2, and combine with machine vision image feature extraction method for hairiness detection. Finally, the modified MobileNetV2 network is used as the backbone of YOLOX network, making the YOLOX is lighter and achieve more efficiently stain detection of tube yarn. Then, the detection results are used to determine whether the glass tube yarn has passed. In addition, we establish an effective and sufficient amount of tube yarn defects dataset. The experimental results show that the proposed hairiness detection algorithm achieve 96% accuracy with 160+ FPS, and the surface stain detection algorithm achieve 0.89 mAP with 71+ FPS on the tube yarn dataset. The system is efficient, precise, and can be applied to actual production.

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Section: Tube Yarn Hairiness Defect Detectionmentioning

confidence: 99%

A defect detection system of glass tube yarn based on machine vision

Bao

Jing

Xie

2023

Journal of Industrial Textiles

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“…Penza and colleagues [26] presented an advanced strategy for 3D reconstruction, utilizing a sliding window approach and census transform characteristics. In recent years, Guang Zhong Yang et al [27] of Imperial College London have been devoted to research on the surgical navigation of stereoscopic endoscope vision and have also carried out much research in this field, and achieved certain results [28,29].…”

Section: Stereo Endoscope Vision and 3d Reconstructionmentioning

confidence: 99%

Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

Tian,

Ma,

et al. 2023

Electronics

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Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions.

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