Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstl...Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstly,a multiplexed aggregated feature extraction network is proposed using residual bottleneck block(RES-Bottleneck)and middle partial-convolution(MP-Conv)to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information.Secondly,a lightweight feature fusion network is designed using scale-fuse concatenation(SF-Cat)and triple-scale sequence feature fusion(TSSF)module to merge multi-scale feature maps comprehensively.Depthwise convolution(DWConv)and GhostNet lighten the network,while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module(C3-GN)reduces missed detections by suppressing irrelevant background information.Finally,soft non-maximum suppression(Soft-NMS)is used in the post-processing stage to improve the problem of misdetection of dense disease sites.The results show that the MSL-Net improves mean average precision at intersection over union of 0.5(mAP@0.5)by 2.0%over the baseline you only look once version 5s(YOLOv5s)and reduces parameters by 44%,reducing computation by 27%,outperforming other state-of-the-art(SOTA)models overall.This method also shows excellent performance compared to the latest research.展开更多
为了优化利用IP层和WDM层网络资源,在WDM网络集成辅助图模型的基础上,提出了一种面向IP/GMPLS over WDM网络的基于代价的优化综合路由算法,即CIR(Cost-based Integrated Routing)。该算法将IP层和WDM层资源可用信息以代价函数形式给出,...为了优化利用IP层和WDM层网络资源,在WDM网络集成辅助图模型的基础上,提出了一种面向IP/GMPLS over WDM网络的基于代价的优化综合路由算法,即CIR(Cost-based Integrated Routing)。该算法将IP层和WDM层资源可用信息以代价函数形式给出,并将因网络拓扑结构和网络负载分布不均衡等产生的瓶颈链路以及带宽碎片问题也统一纳入考虑,由此将LSP建立问题转化为在集成辅助图上找出一条源、目的节点之间的最短通路问题。仿真结果表明:CIR算法有效地实现了IP和WDM两层资源的联合优化,提高了网络资源利用,降低了网络阻塞率。展开更多
文摘Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstly,a multiplexed aggregated feature extraction network is proposed using residual bottleneck block(RES-Bottleneck)and middle partial-convolution(MP-Conv)to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information.Secondly,a lightweight feature fusion network is designed using scale-fuse concatenation(SF-Cat)and triple-scale sequence feature fusion(TSSF)module to merge multi-scale feature maps comprehensively.Depthwise convolution(DWConv)and GhostNet lighten the network,while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module(C3-GN)reduces missed detections by suppressing irrelevant background information.Finally,soft non-maximum suppression(Soft-NMS)is used in the post-processing stage to improve the problem of misdetection of dense disease sites.The results show that the MSL-Net improves mean average precision at intersection over union of 0.5(mAP@0.5)by 2.0%over the baseline you only look once version 5s(YOLOv5s)and reduces parameters by 44%,reducing computation by 27%,outperforming other state-of-the-art(SOTA)models overall.This method also shows excellent performance compared to the latest research.
文摘为了优化利用IP层和WDM层网络资源,在WDM网络集成辅助图模型的基础上,提出了一种面向IP/GMPLS over WDM网络的基于代价的优化综合路由算法,即CIR(Cost-based Integrated Routing)。该算法将IP层和WDM层资源可用信息以代价函数形式给出,并将因网络拓扑结构和网络负载分布不均衡等产生的瓶颈链路以及带宽碎片问题也统一纳入考虑,由此将LSP建立问题转化为在集成辅助图上找出一条源、目的节点之间的最短通路问题。仿真结果表明:CIR算法有效地实现了IP和WDM两层资源的联合优化,提高了网络资源利用,降低了网络阻塞率。
文摘目的针对肝脏肿瘤检测方法对小尺寸肿瘤的检测能力较差和检测网络参数量过大的问题,在改进EfficientDet的基础上,提出用于肝脏肿瘤检测的多尺度自适应融合网络MAEfficientDet-D0(multiscale adaptive fusion network-D0)和MAEfficientDet-D1。方法首先,利用高效倒置瓶颈块替换EfficientDet骨干网络的移动倒置瓶颈块,在保证计算效率的同时,有效解决移动倒置瓶颈块的挤压激励网络维度和参数量较大的问题;其次,在特征融合网络前添加多尺度块,以扩大网络有效感受野,提高体积偏小病灶的检测能力;最后,提出多通路自适应加权特征融合块,以解决低层病灶特征图的语义偏弱和高层病灶特征图的细节感知能力较差的问题,提高了特征的利用率和增强模型对小尺寸肝脏肿瘤的检测能力。结果实验表明,高效倒置瓶颈层在少量增加网络复杂性的同时,可以有效提高网络对模糊图像的检测精度;多通路自适应加权特征融合模块可以有效融合含有上下文信息的深层特征和含有细节信息的浅层特征,进一步提高了模型对病灶特征的表达能力;多尺度自适应融合网络对肝脏肿瘤检测的效果明显优于对比模型。在LiTS(liver tumor segmentation)数据集上,MAEfficientDet-D0和MAEfficientDet-D1的mAP(mean average precision)分别为86.30%和87.39%;在3D-IRCADb(3D image reconstruction for comparison of algorithm database)数据集上,MAEfficientDet-D0和MAEfficientDet-D1的mAP分别为85.62%和86.46%。结论本文提出的MAEfficientDet系列网络提高了特征的利用率和小病灶的检测能力。相比主流检测网络,本文算法具有较好的检测精度和更少的参数量、计算量和运行时间,对肝脏肿瘤检测模型部署于嵌入式设备和移动终端设备具有重要参考价值。
