基于改进YOLOv4-Tiny的缝纫线迹 质量检测方法  

Stitch quality detection method based on improved YOLOv4-Tiny

作　　者：马创佳 齐立哲 高晓飞 王子恒 孙云权 MA Chuangjia;QI Lizhe;GAO Xiaofei;WANG Ziheng;SUN Yunquan(Academy for Engineering&Technology,Fudan University,Shanghai 200433,China)

机构地区：[1]复旦大学工程与应用技术研究院,上海200433

出　　处：《纺织学报》2023年第8期181-188,共8页Journal of Textile Research

基　　金：上海市市级科技重大专项项目(2021SHZDZX0103);广东季华实验室重大共性关键技术及应用示范研究科研项目(Y80311W180)。

摘　　要：针对人工检测缝纫线迹质量效率低下、当前算法在缝纫线迹质量检测应用上难以检测与面料颜色相近的线迹以及易受面料褶皱、光照变化等因素干扰的问题,提出一种改进的YOLOv4-Tiny目标检测模型,实现缝纫线迹针脚点的识别和定位,进而实现质量检测。首先在YOLOv4-Tiny中引入用SoftPool改进的卷积注意力机制,加强网络对线迹特征的注意;然后在YOLO检测头前引入由SoftPool组成的Soft-SPPF模块,实现模型在检测中对多尺度特征的利用;最后,利用改进后的算法输出针脚点的数量和坐标信息,计算线迹针脚点的密度和均匀度。实验结果表明:在自建数据集上,所提算法的平均精度达到85.50%,检测时间为15.9 ms,相比原算法和常用的目标检测模型更适用于缝纫线迹检测,且该方法计算所得的线迹密度结果与人工检测的差值在0.6针/(10 cm)内,均匀度计算结果相近,满足实际检测精度要求。Objective It is reported that manual quality detection of sewing stitch is inefficient and that the existing algorithms are difficult to detect the sewing stitch and the detection is easily interfered by factors such as fabric wrinkles and illumination changes.An improved YOLOv4-Tiny object detection algorithm is proposed to recognize and locate the sewing stitch points.The number and position information of the sewing stitch points yielded by the model are used for sewing quality detection.Method The convolutional attention mechanism improved by SoftPool was introduced in YOLOv4-Tiny to enhance the object detection network’s attention to the features of sewing stitch points,before a Soft-SPPF module composed of SoftPool was introduced in front of YOLO Head to realize the model′s utilization of multi-scale features in prediction.The improved algorithm was used to calculate the number and coordinate information of all sewing stitch points in the sewing stitch image,together with the density and uniformity of stitch points.Results The improved YOLOv4-Tiny algorithm and other object detection models were trained by data augmentation on a self-built sewing dataset(Fig.2)and converge after 150 epochs of training(Fig.7),and were tested on a test set.By comparing the improved YOLOv4-Tiny algorithm,MobileNet-SSD,YOLOv5s and the original YOLOv4 algorithm,the detection performance in sewing stitch points of the improved method was notably improved.The improved YOLO-Tiny algorithm achieved a mean average accuracy of 85.50%and a detection time of 15.9 ms(Tab.3),which is more suitable than the original algorithm for sewing stitch detection.In addition(Fig.8 and Tab.2),the improved YOLOv4-Tiny algorithm was found to obtain accurately information on the number and coordinates of sewing stitch points in the images of the five sewing stitch types,and density and uniformity were obtained by calculating the number of stitch points per 10 cm and the average relative error between the distance of all adjacent stitch points and th

关 键 词：缝纫线迹 质量检测 YOLOv4-Tiny 卷积注意力机制 快速空间金字塔池化 服装质量 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TS941.79[自动化与计算机技术—控制科学与工程]