Research on multi label feature recognition of bobbin and yarn based on YoloColor-Net

Abstract

In order to improve the automatic production efficiency of the textile workshop and reduce the labor cost and error rate, this study designed a multi label recognition model YoloColor-Net based on deep learning, which aims to realize the automatic detection and recognition of the bobbin shape and yarn color on the yarn frame in the textile workshop. Firstly, according to the research needs, a dataset sample containing 12,173 textile bobbin images was collected and constructed independently. Then, the traditional yolov5 model is improved by designing the convolution network of yarn color recognition, which solves the problem of missing detection of the bobbin when detecting the bobbin shape and yarn color at the same time. Secondly, the lightweight DSConv module is used to replace the ordinary convolution of the Backbone layer to reduce the parameters of YoloColor-Net and improve the running speed. Finally, the improved attention mechanism (ICBAM) is added to the Backbone and Neck layers of YoloColor-Net to improve the accuracy of bobbin recognition. The experimental results show that the detection accuracy of the improved YoloColor-Net model is 99.3%, the number of model parameters is reduced by 10.4%, and the GFLOPS is reduced by 17.1%. FPS increased from 43.13 to 67.23, an increase of 55.9%. Therefore, the proposed model can basically meet the task of bobbin automatic detection and recognition, and consider the initial localization deployment.