Fabric defect detection method based on YOLO v4 improved algorithm

A technology for defect detection and algorithm improvement, applied in neural learning methods, calculations, computer components, etc., can solve problems such as easy missed detection of defects, unbalanced aspect ratio, etc.

Active Publication Date: 2021-07-30
ZHEJIANG SCI-TECH UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] Aiming at the above-mentioned problems existing in the prior art, the present invention proposes a fabric defect detection method based on the YOLO v4 improved algorithm, which solves the problem that in the defect detection of fabrics, defects with extremely unbalanced aspect ratios and small scales are easily missed And other issues

Method used

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  • Fabric defect detection method based on YOLO v4 improved algorithm
  • Fabric defect detection method based on YOLO v4 improved algorithm
  • Fabric defect detection method based on YOLO v4 improved algorithm

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Embodiment Construction

[0054] The implementation of the present invention is described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the case of no conflict, the following embodiments and features in the embodiments can be combined with each other.

[0055] This embodiment provides a fabric defect detection method based on the YOLO v4 improved algorithm, referring to figure 1 As shown, the image is a schematic flow chart of the method. refer to figure 2 , the image is an image of the Tianchi cloth detection data set, whic...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
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Abstract

The invention belongs to the technical field of image target detection, and particularly discloses a fabric defect detection method based on a YOLO v4 improved algorithm, and the method introduces a latest lightweight attention module (CA) on a backbone network, not only can capture cross-channel information, but also can capture direction perception and position perception information, the network can carry out emphasized detection on an interested target, and a deformable convolutional network (DCN) is added to enhance the adaptability of the network to flaws with variable shapes, so that the detection accuracy is improved. And for the feature fusion part, adaptive weighted fusion (ASFF) is used on the basis of an original path aggregation network, so that features extracted by each feature layer are fused with different weights before prediction, meanwhile, a cross-stage local network structure (CSP) is used for replacing partial convolution of the feature fusion part, and under the condition that the speed is ensured, and the fabric defect detection accuracy of the network is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image target detection, and in particular relates to a fabric defect detection method based on the YOLO v4 improved algorithm. Background technique [0002] my country is a big exporter of textiles in the world, and the price of textiles in our country is also very competitive in the international market. Relying on advanced textile technology, the output and quality have also been continuously improved, but some defects still inevitably appear in the final product, affecting the appearance of the fabric. In the process of fabric sales, the high quality and aesthetics of the fabric must be guaranteed to win the market. Therefore, fabric defect detection is also an indispensable part of the production process, thereby effectively reducing unqualified fabrics from entering the market. In the past, due to the limitations of technology and hardware facilities, it could only be screened by manual inspection. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06T7/0002G06T5/007G06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30124G06V10/25G06V2201/07G06N3/045G06F18/23213G06F18/253G06F18/214
Inventor 吕文涛余序宜
Owner ZHEJIANG SCI-TECH UNIV
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