LAUSR

This article examines how to accurately measure yarn density for 3-D braided composite preforms in large images. However, due to the complex structure and dense yarns of the preform, there is a lack of effective methods to solve this problem in practical applications. In the end, the task of yarn density measurement is mainly performed by experienced operators. They usually use a ruler to measure the distance between a fixed number of fabrics to calculate density, which is time-consuming and subjective. Therefore, an effective and efficient method for yarn density measurement of 3-D braided composite preforms is demanded. To address this problem, we propose a novel framework combining deep learning with traditional image processing techniques to measure yarn density automatically. First, to ensure the accuracy of yarn detection, we obtain a high-resolution raw image of $2448\times2048$ . The preprocessed images are then fed into our Yarn Detection Network (YDNet) based on the rotation object detection method to detect per segmented yarn. To achieve this, we propose a dilated feature network (DFN) that can obtain features at different scales and improve detection speed. Then, the feature alignment module (FAM) is introduced to achieve higher detection accuracy. Moreover, we design a Yarn Loss (YLoss) to detect yarn boxes more accurately via considering the aspect ratio of the yarns as an important parameter. Then, we design a multilevel rotation mechanism based on the extracted yarns to correct the image and calculate the yarn density accurately. We conduct several experiments to verify the superiority of our proposed YDNet, and the accuracy and effectiveness of the proposed framework for the automatic yarn density measurement. The experimental results show that our method is superior to other state-of-the-art methods.