Research On Embedded Detection System Of Woven Fabric Defects Based On YOLOX

With the continuous development of machine vision technology,automatic detection technology in various fields has gradually developed and has become mature in many fields.In the textile field,the automatic detection of fabric defects has not been realized yet.At present,there is more and more research on fabric defect detection algorithms based on depth learning technology,but the number of research on fabric defect detection systems based on depth learning algorithms is far away.This is related to the actual deployment difficulty of the fabric defect detection system hardware,which also shows that the current defect detection algorithm can not be well applied to the actual detection.Therefore,this paper studies the embedded detection system of fabric defects based on a depth learning algorithm designs an embedded detection system that is convenient for actual deployment and designs a lightweight defect detection algorithm combined with the application scenario.This paper mainly studies the following aspects:(1)Research on embedded detection system of woven fabric defectsThis paper proposes a new embedded detection system based on the Android operating system.The system is divided into an image acquisition module,defect detection module,result in the storage module.The image acquisition module collects the fabric image to be detected in realtime through a CMOS camera,and transfers the collected image information to the defect detection module for real-time detection;The defect detection module detects the processed fabric image information to obtain the defect category and location information;The result storage module stores the defect category,location information and defect image.(2)Research on woven fabric defect detection algorithm based on depth learningFirstly,based on the weak computing power of the embedded system processor and the large difference in woven fabric defect size,the YOLOX algorithm in-depth learning is selected as the defect detection algorithm;Then,the FM module is designed to optimize the defect detection algorithm according to the two characteristics of woven fabric defects which are arranged along the warp and weft direction and there are many small defects;Finally,a lightweight scheme of defect detection model is designed,and the lightweight algorithm model is deployed to the detection system.(3)Analysis of the correlation effect of the detection systemThe defect detection system is deployed to embedded devices,and the detection results of two lightweight detection algorithms are compared and analyzed;Then the influence on the detection effect of the detection model is analyzed from two aspects of defect morphology and light strip.First,test and deploy the detection effects of different depth detection models in embedded systems;After that,three groups of height width ratio,three groups of size and three groups of light intensity defects are used to test the model.Finally,other data sets are used to test the detection effect of the defect detection model,and an optimization scheme is given.