Clothes identification and classification method based on F-CDSSD

A technology for identifying, classifying and clothing, applied in the field of Internet of Things, can solve the problems of poor image recognition effect, increase multi-scale targets, large structure, etc., to improve the recognition speed, improve the training speed and recognition accuracy, and reduce the amount of calculation.

Inactive Publication Date: 2020-01-31
CHINA UNIV OF PETROLEUM (EAST CHINA)
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

With YOLO as the main body and improved, the proposed SSD increases the detection of multi-scale targets, which largely solves the problems of YOLO's difficulty in detecting small targets and inaccurate positioning.
However, there are too many inappropriate default boxes in the SSD algorithm, which will cause a lot of waste of

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Clothes identification and classification method based on F-CDSSD
  • Clothes identification and classification method based on F-CDSSD

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0021] Such as figure 1 As shown, the flow of the clothing recognition and classification framework based on F-CDSSD mainly includes four parts: data acquisition module, object recognition and classification module, detection result cache module and model optimization module.

[0022] Below, the specific process of the F-CDSSD-based clothing recognition sub-framework is described in detail:

[0023] Step (1), the data acquisition module is turned on in real t...

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
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an F-CDSSD-based lightweight clothes identification and classification method, which comprises that a data enhancement technology is adopted to solve the problem that a trainingdata set is too small, and avoiding an over-fitting phenomenon; a traditional VGG-16 network is replaced with ShuffleNet, and fuzzy processing is performed on feature maps output by all convolution layers, so that the complexity of a network structure is reduced, and the network structure can run on a light computing platform; the size, the aspect ratio and the number of default boxes are adjusted, so that the precision and the efficiency of the neural network are improved; a fuzzy theory is introduced for an unclear image, so that the recognition precision of a low-quality clothes image is improved; active learning and iterative learning are realized, a model detection result is fully utilized, and continuous optimization of a neural network model is realized.

Description

technical field [0001] The invention relates to the Internet of Things, clothing identification and classification based on deep learning, active learning, fuzzy theory, concurrent execution and caching technology, and specifically relates to a clothing identification and classification method based on F-CDSSD. Background technique [0002] In recent years, deep learning has developed rapidly, pushing target detection and classification to a new level. The convolutional neural network has strong adaptability to factors such as geometric transformation, deformation, and illumination of the target, and effectively overcomes the recognition resistance caused by the changing appearance of clothing. It can automatically generate descriptions of corresponding features according to the data input into the network, and has high flexibility and universality. At present, target detection in images is mainly divided into single-stage methods (such as SSD, YOLO) and two-stage methods (...

Claims

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
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/24G06F18/214
Inventor 赵宏伟张卫山于强
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap