结合显微视觉和注意力机制的毛羽检测方法

Detection of yarn hairiness combining microscopic vision and attention mechanism

毛羽瑕疵由于尺寸微小,极易发生漏检和误检,是化纤丝饼检测中的难点。为了准确高效地检测丝饼的外观瑕疵,搭建了基于显微视觉的毛羽瑕疵图像采集平台,提出加入注意力机制和特征融合的瑕疵检测算法(CenterNet-CBAM)。采集了毛羽和断线两类易混淆的目标图像,构建基于CenterNet-CBAM的目标检测模型,并与CenterNet、YOLOv4、Faster R-CNN、SSD四种目标检测算法进行比较。实验结果表明,工业显微相机能有效获取毛羽特征,CenterNet-CBAM在毛羽、断线两类目标的实验检测结果的准确率分别为94.00%和93.57%,召回率分别为86.75%和92.16%,AP值分别为92.93%和92.91%,两类mAP值均为92.92%,优于其他算法,验证了实验方法的有效性。

Broken ends of yarns are difficult to detect due to their small features, which are easily overlooked and confused. To detect defects accurately and efficiently in the appearance of polyester yarn packages, an image acquisition platform with a microscopic camera was designed, and a defect detection algorithm(CenterNet-CBAM) with an attention mechanism and feature fusion was proposed. Two types of confusing target images, yarn hairiness, and broken ends were collected to design an objective model of CenterNet-CBAM, which was compared with four other object detection algorithms, CenterNet, YOLOv4, Faster R-CNN, and SSD. The results show that the industrial microscope camera can acquire the feature of yarn hairiness. The precision of CenterNet-CBAM for yarn hairiness and broken ends is 94.00%and 93.57%, the recall rates are 86.75% and 92.16%, AP values are 92.93% and 92.91%, respectively, and the mAP values are 92.92%. Better results in defect detection compared to other algorithms demonstrate the validity of the experiment method.