Research On Fabric Defect Detection Algorithm Based On Improved Feature Pyramid

Clothing,food and shelter are vital to people's daily lives.As far as clothing is concerned,cloth is an indispensable raw material for making clothing.In the fabric production process,improper mechanical operation and objective environment cause the textile machine to cause defects on the surface of the fabric during the production process.Whether there is a defect on the surface of the fabric is a very critical factor that determines the quality of the fabric.However,the current inspection methods of many enterprises are still manual inspection,and workers' long-term use of naked eyes will cause visual fatigue,so that the detection speed is reduced,which is not suitable for practical produce.Therefore,it is very important to find a more automated technology for cloth inspection.Fabric defect detection methods can be roughly divided into four categories,namely statistical methods,model methods,spectrum methods and machine learning methods.However,statistical methods,model methods and spectrum methods have problems such as large calculation volume and poor real-time performance.They are not suitable for use in actual production.With the development of current computing technology,especially the development of deep learning,it provides updated and more reliable technical support for fabric defect detection.In order to improve the performance of fabric defect detection,this paper proposes an improved fabric defect detection method for feature pyramids.Aiming at the problems of variable target size,irregular shape,and many small targets in cloth defect detection,this paper applies a object detection algorithm based on deep learning to fabric defect detection.Faster R-CNN is one of the classic algorithms of object detection algorithms based on deep learning.FPN is based on the improvement of Faster R-CNN.It builds multi-scale feature maps to form feature pyramid structures.It combines high-level features that contain more classified semantic information with low-level features that containing more location information,texture color,and other information.Experiments show that there will be certain problems in directly applying FPN to fabric defect detection,including missing targets,error detection,etc.In response to these problems,this article has made some improvements to FPN.The main improvement points such as : Aiming at the irregularity of the shape of the defect in the dataset image,a deformable convolution is introduced in this paper to adapt to defects with different shapes in the dataset,improve the model's ability to detect defects of different shapes and enhance the model's generalization performance;At the same time,for the simple feature pyramid structure,there will be an imbalance of feature levels,which will make the features of the current layer pay more attention to the semantic information of adjacent layers.This article introduces the Balanced Feature Pyramid module.This paper improves the detection performance by performing feature fusion on different levels of features,and then assigning the fused features to each level so that the features of each layer have information from each layer,thereby improving detection performance.The experimental data in this paper comes from the Tianchi competition cloth defect detection data set.Through experimental analysis,the mAP values of Faster R-CNN and FPN on the data set are 58.61 and 59.69,respectively.The improved algorithm's mAP value is 64.30,which is 5.69 points higher than Faster R-CNN and 4.61 points higher than the FPN.In the detection of small targets,the mAP value of the improved algorithm in this paper is 2.1 points higher than the FPN.Experimental results show that the improved algorithm has a good effect on cloth defect detection.