Convolutional neural network based ribbon edge burr defect detection method

Abstract

The invention discloses a convolutional neural network based ribbon edge burr defect detection method. A camera collects a ribbon picture, the edge is extracted from a ribbon, and a sample picture with a burr defect and a sample picture without the burr defect are obtained; and the collected sample pictures are detected in a classified way via the convolutional neural network with a multiscale parallel training structure, the convolutional neural network increase the depth and width of the neural network, a full connection layer is removed from a common convolutional neural network, common convolution is converted into sparse connection, and a quasi-optimal local sparse structure via dense components is used to maintain a high computing performance of the neural network. Thus, the blur defect detection method can be used to detect blur defects of the ribbon edge effectively, maintain or reduce the computational complexity of the convolutional neural network, and thus, improve the computing performance.

Description

technical field [0001] The invention relates to the field of detection of webbing edge blemishes, in particular to a detection method for webbing edge blemishes based on a convolutional neural network. Background technique [0002] In the production process of webbing, due to machine failure and yarn problems, the produced webbing will have many defects, and the appearance of webbing is an important factor affecting product quality. Therefore, defect detection has become a key link in the industrial production of webbing. The traditional manual method of webbing defect detection is too labor-intensive and financial, and this method relies too much on the attention and judgment of the inspector. With the continuous development of computer image processing and recognition, the importance of automatic detection of webbing defects has become increasingly prominent. Replace manual inspection. [0003] The traditional automatic detection of webbing defects is mainly through the t...

AI Technical Summary

Problems solved by technology

[0004] In the convolutional neural network, if the hidden layer in the network is increased, the recognition success rate of the network can be further improved by deepening the number of layers of the neural network. The success rate of webbing edge defect detection can be effectively improved by increasing the layer of the network, but at the same time, deepening the number of layers of the neural network will not only increase the total number of parameters in the network, but also make the amount of calculation very large. Consumes more computing resources, which is prone to overfitting. This problem will be more obvious when the number of data sets is small.

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