Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

基于机器视觉的非织造材料疵点高速检测算法改进

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

疵点检测在非织造材料工业中至关重要。随着深度学习和计算机视觉技术的发展,深度学习已广泛应用于非织造材料表面疵点的检测和定位,以保证成品质量。该论文主要研究基于改进NanoDet-Plus模型的非织造材料疵点检测方法,以构建的非织造材料疵点样本为研究对象,在NanoDet-Plus目标检测模型的基础上,结合迁移学习实验对模型中的Backbone、PAFPN和Head网络模型结构进行对比冻结训练,增强模型特征提取能力以提升检测精度。使用半精度量化方法对迁移学习实验后的模型进行优化,降低模型权重与计算量从而提升检测速度。将改进后的模型与原NanoDet-Plus模型、YOLO和SSD等常见的工业化疵点检测算法进行性能对比,验证结果表明,迁移学习与半精度量化相结合的改进方法可使模型满足工业生产的实际需求。