Chemical fiber fluttering quality detection method and system based on convolutional neural network

Abstract

The invention belongs to the technical field of chemical fiber industry, and particularly relates to a chemical fiber fluttering quality detection method and system based on a convolutional neural network. The system comprises an image acquisition and transmission module, a part detection positioning module, a parameter feedback module, a part recognition and judgment module, and a comprehensive judgment module. A CNN deep learning scheme is used for detecting the fiber fluttering abnormity, and a fiber fluttering abnormity detection technology of key part abnormity detection is provided. A dynamic feedback mechanism and a comprehensive evaluation mechanism are adopted; the mode of robot patrol camera snapshot and background analysis is used for replacing manual detection of abnormal conditions such as fiber fluttering and the like.

Description

Technical field [0001] The present invention belongs to the field of chemical fiber industry, and more particularly to a chemical fiber bronching quality detection method and system based on convolutional neural network. Background technique [0002] Chemical fiber products are mainly long-wire cakes. This filament only has a hairdryer and a slice of 60 meters per second. This high-efficient production process is strive for the environment, and there is a "three constant" requirements of constant temperature and humidity. The subtle change of environmental conditions is easy to cause the filament to float from the predetermined track. If the filaments are floating to adjacent tracks, they form a floating phenomenon. If the filaments floats to other places, they form bristles. [0003] There are two main technical programs currently have the main technical solutions: [0004] (1). The use of artificial naked ways and warning: that is, let the workers go to the machine workshop to ...

AI Technical Summary

Problems solved by technology

Although this method liberates labor costs, it brings new problems that the detection accuracy is greatly affected by the environment, the traditional image processing method is less robust, and there are many devices that need to be deployed, and the cost is high

[0006] Although the above method of using image processing can reduce labor costs, its ability to detect abnormalities is greatly affected by the lighting environment, which is very prone to false detection or missed detection, and the number of deployed hardware is huge, and the hardware cost is high.

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