Real-Time Fabric Defect Detection Using Accelerated Small-Scale Over-Completed Dictionary of Sparse Coding

Feng, Tao; Zou, Lian; Jia, Yan; Shi, Wenxuan; Liu, Yifeng; Fan, Cien; Deng, Dexiang

doi:10.5772/62058

Cited by 73 publications

(11 citation statements)

References 17 publications

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“…For automatic defect detection, Feng et al [64] presented a model based on Small-Scale Over-Completed Dictionary (SSOCD) using sparse coding (SC) which is basically hardware accelerated system. From the industrial environment, fabric images are acquired and then training SSOCD of SC of defect-free fabric images takes place (in training, images are preprocessed using Gabor filter and patches are extracted).…”

Section: Sparse-based Operationmentioning

confidence: 99%

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

105

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There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

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Section: Sparse-based Operationmentioning

confidence: 99%

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

105

View full text Add to dashboard Cite

show abstract

“…According to the characteristics of DSP, we design a multicore scheduler algorithm based on TMS320C6678. Based on the multicore DSP applications, static scheduling is usually used, which is called as “the specified core does the specified things.” 36 Sometimes the tasks are statically assigned to processors, and the tasks within each processor are scheduled by a local scheduler, which makes the application development simple and more convenient for debugging. However, static scheduling is against load balancing.…”

Section: Design and Implementationmentioning

confidence: 99%

Classification of foreign fibers using deep learning and its implementation on embedded system

Wei

Deng

Zeng

et al. 2019

International Journal of Advanced Robotic Systems

Self Cite

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In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) þ digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eightcore DSP) indicates the efficiency of the detection, which ensures the real-time application of the system.

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“…Rehabilitation robot has brought the hope to patients with limbs disabled and therapists. At present, the lower limb rehabilitation robot is now a research hotspot, and few restoration robots have reclamation robots have been structured as the ask about achievement [1][2][3][4][5][6]. The commended recuperation robot structure LOKOMAT which was asked about by the Swiss Government Established of Innovation Zurich used suspension device to reinforce or modify human weight, and the calm may do bring down extremity walking walk reclamation getting ready with the help of outside forces [7].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Spatial Four-DOF Lower Limb Rehabilitation Robot

Wang¹

2019

IJATCSE

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Aiming at the existing problem that the majority of lower limb rehabilitation robots can only realize patient's leg to move in the sagittal plane, cannot achieve the patient's leg to move in coronal plane, a spatial four-DOF multi-pose lower limb rehabilitation robot is proposed. The robot could also realize the sitting training posture, the lying training posture and the standing training posture. The robot includes seat, left mechanical leg and right mechanical leg. Each mechanical leg with series-parallel mechanism includes the hip joint with two DOFs, knee joint with one DOF and ankle joint with one DOF which correspond to the hip joint, knee joint and ankle joint of human. This paper describes the mechanical design and kinematic analysis of the rehabilitation robot. The coordinate system of the lower limb rehabilitation robot is established, and the degree of freedom of the spatial leg mechanism is calculated. The inverse kinematics of the single leg mechanism and the forward kinematics of the lower limb rehabilitation robot are analyzed. The workspace of the leg mechanism was calculated. The kinematics analysis of the leg mechanism lays a theoretical base for the future controlling of the robot prototype.

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Real-Time Fabric Defect Detection Using Accelerated Small-Scale Over-Completed Dictionary of Sparse Coding

Cited by 73 publications

References 17 publications

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Classification of foreign fibers using deep learning and its implementation on embedded system

Design and Analysis of a Spatial Four-DOF Lower Limb Rehabilitation Robot

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