基于残差结构的棉花异性纤维检测算法  

Cotton foreign fibers detection algorithm based onresidual structure

作　　者：师红宇[1] 位营杰 管声启[2] 李怡 SHI Hongyu;WEI Yingjie;GUAN Shengqi;LI Yi(School of Computer Science,Xi′an Polytechnic University,Xi′an,Shaanxi 710048,China;School of Mechanical&Electronic Engineering,Xi′an Polytechnic University,Xi′an,Shaanxi 710048,China)

机构地区：[1]西安工程大学计算机科学学院,陕西西安710048 [2]西安工程大学机电工程学院,陕西西安710048

出　　处：《纺织学报》2023年第12期35-42,共8页Journal of Textile Research

基　　金：陕西省重点研发计划项目(2022GY-058);西安市科技创新人才服务企业项目(2020KJRC0022)。

摘　　要：针对棉花中异性纤维检测精度低、异性纤维隐藏或边角位置不易识别等原因导致检测效果不佳的问题,提出一种基于残差结构的棉花异性纤维检测算法。首先,针对异性纤维检测目标,提出一种棉花异性纤维在线检测方案;其次,针对异性纤维颜色、纹理、位置等特征,构建深浅层混合数据集;在此基础上设计了残差结构的异性纤维检测网络模型算法,解决了现有检测算法精度低、异性纤维隐藏或边角位置的问题;最后,将该算法与传统经典算法对比实验。结果表明:在深浅层混合数据集下,与经典算法对比,该算法具有较高的准确性和实时性,其平均检测准确率达到88.48%,1张图像的检测速度为0.019 s,满足工业现场实时检测需求,为棉花中异性纤维检测提供了一种新方法。ed,so there is less semantic information of such small target objects.In this paper,the attention mechanism is introduced into the residual structure,and different weights are given to the feature map to enhance the representation ability of the key feature of foreign fibers.Experimental results showed that the algorithm reported in this paper achieved remarkable success on deep and shallow datasets.The average detection accuracy of the deep and shallow dataset was 88.48%(Tab.3).In addition,the algorithm performed well when processing a single image,with an average detection speed of only 0.019 s per image.In comparison to classical algorithms such as GoogleNet,MobileNetV1,MobileNetV2 and EfficientNet,the algorithm improved accuracy by 10.85%,3.32%,26.47%and 26.96%,respectively.Compared to MobileNetV2 and EfficientNet,the algorithm tested a single image with shorter running times and higher accuracy.Hence,the algorithm offered a balance between high accuracy and moderate detection speed in foreign fiber detection,catering to the real-time requirements of the industry.It addressed the issues of low accuracy in existing detection algorithms and the presence of hidden or corner-positioned foreign fibers.Furthermore,it introduced a novel approach to foreign fiber detection in cotton.Conclusion In this paper,the algorithm not only achieves a high detection effect on shallow cotton foreign fibers dataset,but also achieves a good results on the mixed dataset of deep and shallow layers cotton foreign fiber.However,for the small objects in the deep depth of the actual industry,the method in this paper will also have false detection and missed detection.In the future,the network structure and network parameters will be optimized to improve the real-time detection of foreign fibers in cotton while maintaining a high detection accuracy.

关 键 词：异性纤维检测 棉花 注意力机制 残差结构 深度可分离卷积 网络模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]