A knowledge augmented deep learning method for vision-based yarn contour detection

Xu, Chuqiao; Wang, Junliang; Jiang, Tao; Zhang, Jie; Zheng, Pai

doi:10.1016/j.jmsy.2022.04.006

Cited by 16 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A review of relevant research indicates that the application of knowledge mainly includes the following aspects: (1) Datasets: simulation augmentation [56], empirically controlled sample construction [57], and automatic hard sample generation [58,59], etc. ; (2) Network architectures: fusion learning of multi-modal data with complementary characteristics [60], contrastive, matching or metric learning with manually designed reference patterns [49,50,61], and designing special network layers to represent knowledge models [62,63], etc. ; (3) Tasks: auxiliary tasks in multi-task learning [64,65], a priori based multi-task branch balance [66], etc.…”

Section: Knowledge Application For Vision-based Methodsmentioning

confidence: 99%

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

Zhao,

Kong,

et al. 2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Knowledge Application For Vision-based Methodsmentioning

confidence: 99%

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

Zhao,

Kong,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…-PDC enjoys the general form to organize the encoding of pixel differences, making it possible to incorporate any LBP variant to the convolutional module. While only the orignial LBP [28] and ELBP variants [22] are now explored and show positive effect on the task of edge or contour detection [33,42], we believe more LBP variants can be considered to tackle other different vision tasks. -Like 3D-CDC, PDC can also be generalized to 3D scenes, where the temporal difference cues can be extracted in a more flexible way, rather than only considering the central differences.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

From Local Binary Patterns to Pixel Difference Networks for Efficient Visual Representation Learning

Su¹,

Pietikäinen²,

Liu³

2023

Preprint

View full text Add to dashboard Cite

LBP is a successful hand-crafted feature descriptor in computer vision. However, in the deep learning era, deep neural networks, especially convolutional neural networks (CNNs) can automatically learn powerful task-aware features that are more discriminative and of higher representational capacity. To some extent, such hand-crafted features can be safely ignored when designing deep computer vision models. Nevertheless, due to LBP's preferable properties in visual representation learning, an interesting topic has arisen to explore the value of LBP in enhancing modern deep models in terms of efficiency, memory consumption, and predictive performance. In this paper, we provide a comprehensive review on such efforts which aims to incorporate the LBP mechanism into the design of CNN modules to make deep models stronger. In retrospect of what has been achieved so far, the paper discusses open challenges and directions for future research.

show abstract

“…Chuqiao Xu et al proposed a knowledge-enhancing deep learning method for vision-based yarn profile detection. This method combines traditional image processing techniques and deep learning techniques to improve the accuracy and stability of contour detection by introducing prior knowledge [31].…”

Section: Introductionmentioning

confidence: 99%

Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement

Zheng

Wang

2023

Electronics

View full text Add to dashboard Cite

Industrial CT is useful for defect detection, dimensional inspection and geometric analysis, while it does not meet the needs of industrial mass production because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT-reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor and observe the objects in different angles of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time this source-detector layout strategy has been adopted. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. Our experiment shows that the reconstruction resulting in undersampled projections is highly enhanced using a deep-learning neural network which meets the demand of non-destructive testing. Meanwhile, our proposed system structure can perform quick scans and reconstructions on larger objects. It solves the pain points of limited scan size and slow scanning speed of existing industrial CT scans.

show abstract

A knowledge augmented deep learning method for vision-based yarn contour detection

Cited by 16 publications

References 37 publications

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

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

From Local Binary Patterns to Pixel Difference Networks for Efficient Visual Representation Learning

Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement

Contact Info

Product

Resources

About