Convolutional neural network cloth defect detection method based on extreme learning machine

Abstract

The invention discloses a convolutional neural network cloth defect detection method based on an extreme learning machine. The method comprises the steps of carrying out feature extraction by constructing a convolutional neural network; fusing the extracted features; extracting cloth defect candidate boxes on the fused feature layer; in a cloth defect detection stage, carrying out regression on the extracted cloth defect candidate boxes by using the convolutional neural network; classifying the cloth defect candidate boxes by using an extreme learning machine; calculating loss by combining classification and regression results with real labels of sample pictures; updating the weight in the network by using a random gradient descent method based on the obtained loss; carrying out continuousiterative training until the loss of the network converges to a minimum value or reaches a preset training round number, and thus obtaining a trained cloth defect detection network model based on theextreme learning machine, so cloth defect detection can be carried out. The overall performance of cloth defect detection is effectively improved, and the cloth defect detection method has higher cloth defect detection accuracy.

Description

technical field [0001] The invention relates to the technical field of cloth defect detection, in particular to a cloth defect detection method based on an extreme learning machine-based convolutional neural network. Background technique [0002] In the production process of industrial products, quality control and testing are indispensable. The existence of cloth defects on the product surface will lead to a decrease in product prices, which seriously affects the benefits of product-related industries. Efficient and accurate identification of product cloth defects It has also become a key problem that the industry needs to solve urgently. At present, most of the cloth defect detection of products in the industry is carried out manually. Manual recognition is not only easily affected by human subjective factors, resulting in high recognition error rate and low recognition efficiency, but also easy to damage human health in some extreme environments. cause damage. In order ...

AI Technical Summary

Problems solved by technology

Using this method to detect cloth defects, the accuracy rate is higher than statistical methods and optical methods, but the detection of small cloth defects is often unsatisfactory, and there is a problem of slow detection speed in practical applications

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