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

A technology of convolutional neural network and quality detection method, which is applied in the field of chemical fiber industry, can solve the problems of many devices, easy to generate false detection or missed detection, poor robustness, etc., achieve a high degree of recognition, ensure stability, and improve efficiency effect

Pending Publication Date: 2021-10-08
浙江五疆科技发展有限公司 +1
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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

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

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Embodiment Construction

[0055]Next, the technical solutions in the embodiments of the present invention will be described in contemplation in the embodiment of the present invention, and it is clear that the described embodiments are intended to be in an embodiment of the invention, not all of the embodiments of the invention. Based on the embodiments of the present invention, there are all other embodiments obtained without making creative labor without making creative labor premises.

[0056] In one embodiment, the carrier is used by patrol robot (referring to a device having an audio and audio and various sensors, with a certain intelligent device), after using the HD camera to take a series of machine overall video, combined with video Each frame information is analyzed. Use the currently popular depth learning CNN network to perform an abnormal identification of the key parts of the chemical fiber machine. This approach not only reduces the cost of hardware deployment and human inspection, but also ...

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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 ...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/04G06T7/00G06T7/73G06N3/04G06N3/08
CPCG06Q10/06395G06Q50/04G06N3/04G06N3/08G06T7/0004G06T7/73G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30124Y02P90/30
Inventor 王会成许纪忠朱云强张潮阳刘振轩柏林刘彪
Owner 浙江五疆科技发展有限公司
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