Film defect detection method based on improved attention mechanism

A defect detection and attention technology, applied in the field of video image processing and pattern recognition, can solve the problems of long detection time, small film defect target, complex calculation, etc., to improve the accuracy, difficulty and expressiveness.

Pending Publication Date: 2021-01-15
ZHEJIANG GONGSHANG UNIVERSITY +1
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Problems solved by technology

The two-stage algorithms mainly include Fast R-CNN, Faster R-CNN, etc. First, the candidate region generation network (RPN) is used, and then they are further classified and returned. This type of method has high precision, but the calculation is complicated and the detection time is high. It is too long to meet the real-time requirements of industrial production; single-stage algorithms mainly include SSD and YOLO series. In recent years, the accuracy of single-stage detection algorit

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  • Film defect detection method based on improved attention mechanism
  • Film defect detection method based on improved attention mechanism
  • Film defect detection method based on improved attention mechanism

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[0030] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] The present invention provides a method for thin film defect detection based on an improved attention mechanism. The workflow of the thin film inspection system is as follows figure 1 shown, the steps are as follows:

[0032] (1) The system reads the film image in real time;

[0033] (2) Input the image into the network model for forward reasoning;

[0034] (3) The system judges whether there is a defect in the film image according to the inference result, if there is a defect, then go to step (4), otherwise go to step (5);

[0035] (4) The system marks the defect with a rectangular frame, and prompts the image to have a defect;

[0036] (5) The system judges whether there are still unread images, and if so, returns to step (1), otherwise ends th...

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Abstract

The invention discloses a film defect detection method based on an improved attention mechanism. The method comprises the following steps: reading a film image in real time; inputting the image into anetwork model for reasoning; and judging whether flaws exist in the thin film image or not according to an inference result, if so, automatically marking by using a red rectangular frame, and otherwise, reading the next thin film image. According to the invention, the YOLOv5 network structure is simplified and modified, and the lightweight attention module is added to the network, so that the defect detection accuracy can be effectively improved on the premise of not influencing the speed; the method is used for industrial film defect detection, the quality of film products can be effectivelyimproved, manual intervention is not needed, and labor and time cost is saved.

Description

technical field [0001] The invention belongs to the field of video image processing and pattern recognition in computer vision, and relates to a film defect detection method based on deep learning. Background technique [0002] With the increasing demand for films in the international and domestic film markets and the increasingly fierce competition within the film industry, more and more film manufacturers have begun to use production lines with wider widths and faster production speeds to reduce costs and increase production. efficiency. However, in the actual production process of the film, due to the influence of various factors, defects such as holes, mosquitoes, black spots, crystal spots, scratches, spots, etc. will appear on the surface of the film, which seriously affects the quality of the film and brings manufacturers unnecessary loss. The traditional manual detection method is susceptible to subjective factors and lacks consistency; and human eyes are prone to ...

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Application Information

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IPC IPC(8): G06T7/00G06N3/08G06N3/04
CPCG06T7/0004G06N3/08G06T2207/20081G06T2207/20084G06T2207/30108G06N3/045Y02P90/30
Inventor 王慧燕沈秋芳何浩
Owner ZHEJIANG GONGSHANG UNIVERSITY
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