基于图像处理与深度学习方法的棉纤维梳理过程纤维检测识别技术

针对棉纤维梳理过程中高速摄像机对锡林表面拍摄得到的图像无法人眼识别的问题,使用图像处理与深度学习结合的算法,通过一系列检测流程实现人眼的辅助识别。采用高速摄像机对梳棉机移动盖板下的锡林表面梳理过程进行拍摄得到数据图像,首先对图像通过多级小波卷积神经网络提取去噪残差,然后使用深度卷积超分辨率重构网络进行超分辨率重构,最后使用一种强噪声条件下的多尺度边缘检测与增强算法进行纤维的勾画,得到可供人眼识别的清晰的纤维图像,最后尝试使用特征增强后的图像样本进行循环生成对抗网络的训练,得到更连续清晰的纤维提取结果。研究表明,该图像处理流程提高了对梳理过程纤维的检测识别效果,为纤维梳理领域的研究提供了一种新的思路。

Fiber detection and recognition technology in cotton fiber carding process based on image processing and deep learning

Aiming at the problem that the images obtained by the high-speed camera on the surface of the cylinder during the cotton fiber carding process cannot be recognized by the human eye, algorithms that combine image processing and deep learning were employed to assist human identification through a series of detection processes. The image data was derived from the high-speed video camera data of the carding process of the cylinder surface under the moving cover of the card. The specific implementation process was to first extract the denoising residuals from the image through a multi-level wavelet convolutional neural network, then use the deep convolutional networks for super-resolution reconstruction, and finally use a multi-scale edge detection and enhancement algorithm under strong noise to sketch the fibers. Through the processing of these three steps in the algorithm, a clear fiber image recognizable by the human eyes was obtained. Feature-enhanced image samples were used to train the cycle-consistent adversarial network to obtain more continuous and clear fiber extraction results. The results from the research demonstrate that the proposed processing procedure improves the detection and recognition effect of fibers during carding, and provides a new idea for the research in the field of carding.