食源性蛋白淀粉样纤维化聚集具有独特的结构特性,蚕豆11S蛋白(fava bean 11S protein,FP)作为一种可持续蛋白资源,表现出巨大的潜力。该研究探究了蚕豆11S蛋白淀粉样纤维化聚集(fibrotic aggregation of 11S protein in fava bean,FPF)...食源性蛋白淀粉样纤维化聚集具有独特的结构特性,蚕豆11S蛋白(fava bean 11S protein,FP)作为一种可持续蛋白资源,表现出巨大的潜力。该研究探究了蚕豆11S蛋白淀粉样纤维化聚集(fibrotic aggregation of 11S protein in fava bean,FPF)在形成过程中的动态演变,包括其结构表征和功能特性。6 g/100 mL的FP通过酸热处理(pH 2,85℃)不同时间(0~24 h)后得到FPF。处理后的样品通过硫黄素T、荧光、二酪氨酸、透射电子显微镜、傅里叶红外光谱等进行结构表征,结果表明FP先在酸热过程中水解成多肽,再自组装成富含β-折叠结构的FPF(由0 h的34.44%增加到24 h的45.89%)。通过起泡性、乳化性和凝胶特性等对FPF功能特性进行表征,与FP相比,反应24 h后的FPF具有更好的起泡性、乳化性和凝胶特性。此外,FPF在体外细胞实验中没有表现出细胞毒性。研究结果为FPF的形成规律提供了理论支撑。展开更多
针对印刷电路板(printed circuit board,PCB)缺陷目标小导致识别精度低的问题,提出基于三重注意力跨阶段连接-你只看一次版本11小型(triplet attention and cross stage connections-you only look once version 11 small,TAC-YOLOv11s)...针对印刷电路板(printed circuit board,PCB)缺陷目标小导致识别精度低的问题,提出基于三重注意力跨阶段连接-你只看一次版本11小型(triplet attention and cross stage connections-you only look once version 11 small,TAC-YOLOv11s)的PCB缺陷检测与实例分割算法。首先,设计了跨阶段部分连接(cross stage partial connections,CSPC)特征提取网络,增强网络的特征表达能力;其次,增加了小目标分割头(small object segmentation head,SO)模块,提高对小目标的检测和分割能力;然后,加入了三重注意力(triplet attention,TA)机制,增加对小目标的定位和捕获;最后,采用广义交并比(generalized intersection over union,GIoU)损失函数,优化算法性能。结果表明,与原始YOLOv11s算法相比,TAC-YOLOv11s算法的边界框和掩膜精确率分别提升了11.1%和8.2%,50%交并比阈值下的平均精确率均值分别提升了30.4%和34.3%,证明了算法的优越性。TAC-YOLOv11s算法对实现PCB缺陷的高精度检测与分割具有重要意义。展开更多
Road defect detection plays a pivotal role in enhancing traffic safety,optimizing urban management,and fostering sustainable urban development.Nevertheless,the limited availability of detection resources constrains th...Road defect detection plays a pivotal role in enhancing traffic safety,optimizing urban management,and fostering sustainable urban development.Nevertheless,the limited availability of detection resources constrains the deployment and effectiveness of many existing models.To address this challenge,we propose SCD(space-to-depth convolution,ConvTranspose,distance intersection over union(DIoU))-YOLO11s(you only look once version 11 small),an enhanced variant of YOLO11s.The proposed method substantially improves detection accuracy and model adaptability for small-scale defects by integrating the SPD-Conv(space-to-depth convolution)module to capture fine-grained target features,the ConvTranspose module to mitigate resolution degradation of feature maps induced by repeated downsampling,and DIoU loss function to refine multi-scale target localization.Experimental evaluations conducted on the RDD2022 public dataset demonstrate that SCD-YOLO11s achieves a 3.4%improvement in detection accuracy and a 3.5%increase in mAP@0.5%,with only a 1.9 M parameter overhead.These findings highlight the effectiveness and practical significance of the proposed approach in advancing automatic road defect detection systems.展开更多
文摘针对印刷电路板(printed circuit board,PCB)缺陷目标小导致识别精度低的问题,提出基于三重注意力跨阶段连接-你只看一次版本11小型(triplet attention and cross stage connections-you only look once version 11 small,TAC-YOLOv11s)的PCB缺陷检测与实例分割算法。首先,设计了跨阶段部分连接(cross stage partial connections,CSPC)特征提取网络,增强网络的特征表达能力;其次,增加了小目标分割头(small object segmentation head,SO)模块,提高对小目标的检测和分割能力;然后,加入了三重注意力(triplet attention,TA)机制,增加对小目标的定位和捕获;最后,采用广义交并比(generalized intersection over union,GIoU)损失函数,优化算法性能。结果表明,与原始YOLOv11s算法相比,TAC-YOLOv11s算法的边界框和掩膜精确率分别提升了11.1%和8.2%,50%交并比阈值下的平均精确率均值分别提升了30.4%和34.3%,证明了算法的优越性。TAC-YOLOv11s算法对实现PCB缺陷的高精度检测与分割具有重要意义。
基金Supported by the National Science Foundation of China(No.62571164)the Natural Science Foundation of Heilongjiang Province(PL2024F025)the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Road defect detection plays a pivotal role in enhancing traffic safety,optimizing urban management,and fostering sustainable urban development.Nevertheless,the limited availability of detection resources constrains the deployment and effectiveness of many existing models.To address this challenge,we propose SCD(space-to-depth convolution,ConvTranspose,distance intersection over union(DIoU))-YOLO11s(you only look once version 11 small),an enhanced variant of YOLO11s.The proposed method substantially improves detection accuracy and model adaptability for small-scale defects by integrating the SPD-Conv(space-to-depth convolution)module to capture fine-grained target features,the ConvTranspose module to mitigate resolution degradation of feature maps induced by repeated downsampling,and DIoU loss function to refine multi-scale target localization.Experimental evaluations conducted on the RDD2022 public dataset demonstrate that SCD-YOLO11s achieves a 3.4%improvement in detection accuracy and a 3.5%increase in mAP@0.5%,with only a 1.9 M parameter overhead.These findings highlight the effectiveness and practical significance of the proposed approach in advancing automatic road defect detection systems.
