材料专业研究生学位课程的教学需要不断改革和探索。总结硕士研究生学位课程"材料结构与性能"的教学目前存在的不足,深入分析美国加州大学圣地亚哥分校(University of California,San Diego)研究生课程教学特点,从教学理念与...材料专业研究生学位课程的教学需要不断改革和探索。总结硕士研究生学位课程"材料结构与性能"的教学目前存在的不足,深入分析美国加州大学圣地亚哥分校(University of California,San Diego)研究生课程教学特点,从教学理念与教学目标、教学方式与教学内容、教学考核与教学评价等方面,对"材料结构与性能"课程的教学改革进行了有益的探索,力求不断提高该课程的授课质量与水平,为省内外相关专业的研究生课程建设提供一定的参考与借鉴。展开更多
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,maki...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.展开更多
文摘材料专业研究生学位课程的教学需要不断改革和探索。总结硕士研究生学位课程"材料结构与性能"的教学目前存在的不足,深入分析美国加州大学圣地亚哥分校(University of California,San Diego)研究生课程教学特点,从教学理念与教学目标、教学方式与教学内容、教学考核与教学评价等方面,对"材料结构与性能"课程的教学改革进行了有益的探索,力求不断提高该课程的授课质量与水平,为省内外相关专业的研究生课程建设提供一定的参考与借鉴。
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘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.
