Deep learning(DL)models have demonstrated significant value in computational perception,superresolution imaging,ultra-precision measurement,and photonic device design.In optical communication signal recognition,DL mod...Deep learning(DL)models have demonstrated significant value in computational perception,superresolution imaging,ultra-precision measurement,and photonic device design.In optical communication signal recognition,DL models can achieve fast and accurate identification.However,in high-capacity optical communication systems represented by orbital angular momentum(OAM)beams,neural networks often suffer from excessive parameter sizes and demand large training datasets.To address these challenges,we report a lightweight MobileNetV1 model optimized with efficient channel attention to perform OAM mode recognition after transmission through free space and underwater tank environments.Experimental results show that in simulated small-sample classification tasks with five samples per class,the proposed model achieves an accuracy of 99.67%even under moderate turbulence conditions,outperforming four other DL models.In addition,for experimental datasets collected from both atmospheric turbulence and underwater environments,the model consistently achieves recognition accuracies exceeding 90%,demonstrating strong generalization ability and a 77%reduction in parameter count compared to traditional convolutional neural network(CNN)-based DL models.We provide a new approach for deploying lightweight DL algorithms on resource-constrained embedded optical signal detection devices.展开更多
为了提高对管制物品的检测精度,本文提出一种结合RFB(receptive field block)网络结构和特征融合的目标检测算法。首先对采集的安检数据进行无效内容剔除、滤波;接着对安检数据进行人工标注和数据增强;然后在MobileNetV3-SSD算法的基础...为了提高对管制物品的检测精度,本文提出一种结合RFB(receptive field block)网络结构和特征融合的目标检测算法。首先对采集的安检数据进行无效内容剔除、滤波;接着对安检数据进行人工标注和数据增强;然后在MobileNetV3-SSD算法的基础上,通过引入RFB网络改进其网络结构,以加强网络的特征提取能力,并利用特征融合的方法提高模型的小目标检测能力;最后,构建了一个安检数据集SCCI2020来验证算法的性能,该数据集包含91767张图片。实验结果表明,本算法在安检数据集SCCI2020上的检测精度为87.0%,比MobileNetV3-SSD算法的检测精度高2.7个百分点;在COCO2014和COCO2017通用数据集上的检测精度分别为21.9%和23%,相对于VGG16-SSD、MobileNetV3-SSD算法均有一定提升。展开更多
农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差...农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差神经网络(Residual Network,ResNet)50、视觉几何群网络(Visual Geometry Group Network,VGG)16以及微调预训练模型VGG16共4种网络模型二分类农田害虫图片集。由于样本数据量较少,为防止出现过拟合,使用了数据增强技术,即通过现有训练图片生成更多的训练图片,从而提高泛化能力。实验表明,4种网络模型的准确率分别为88.63%、91.73%、86.49%和90.13%,在农田害虫识别中均具有较好的实际应用效果。展开更多
基金supported by the China Postdoctoral Science Foundation(Grant No.2024M760415)the Natural Science Foundation of Guangdong Province(Grant No.2022A1515140118).
文摘Deep learning(DL)models have demonstrated significant value in computational perception,superresolution imaging,ultra-precision measurement,and photonic device design.In optical communication signal recognition,DL models can achieve fast and accurate identification.However,in high-capacity optical communication systems represented by orbital angular momentum(OAM)beams,neural networks often suffer from excessive parameter sizes and demand large training datasets.To address these challenges,we report a lightweight MobileNetV1 model optimized with efficient channel attention to perform OAM mode recognition after transmission through free space and underwater tank environments.Experimental results show that in simulated small-sample classification tasks with five samples per class,the proposed model achieves an accuracy of 99.67%even under moderate turbulence conditions,outperforming four other DL models.In addition,for experimental datasets collected from both atmospheric turbulence and underwater environments,the model consistently achieves recognition accuracies exceeding 90%,demonstrating strong generalization ability and a 77%reduction in parameter count compared to traditional convolutional neural network(CNN)-based DL models.We provide a new approach for deploying lightweight DL algorithms on resource-constrained embedded optical signal detection devices.
文摘为了提高对管制物品的检测精度,本文提出一种结合RFB(receptive field block)网络结构和特征融合的目标检测算法。首先对采集的安检数据进行无效内容剔除、滤波;接着对安检数据进行人工标注和数据增强;然后在MobileNetV3-SSD算法的基础上,通过引入RFB网络改进其网络结构,以加强网络的特征提取能力,并利用特征融合的方法提高模型的小目标检测能力;最后,构建了一个安检数据集SCCI2020来验证算法的性能,该数据集包含91767张图片。实验结果表明,本算法在安检数据集SCCI2020上的检测精度为87.0%,比MobileNetV3-SSD算法的检测精度高2.7个百分点;在COCO2014和COCO2017通用数据集上的检测精度分别为21.9%和23%,相对于VGG16-SSD、MobileNetV3-SSD算法均有一定提升。
文摘农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差神经网络(Residual Network,ResNet)50、视觉几何群网络(Visual Geometry Group Network,VGG)16以及微调预训练模型VGG16共4种网络模型二分类农田害虫图片集。由于样本数据量较少,为防止出现过拟合,使用了数据增强技术,即通过现有训练图片生成更多的训练图片,从而提高泛化能力。实验表明,4种网络模型的准确率分别为88.63%、91.73%、86.49%和90.13%,在农田害虫识别中均具有较好的实际应用效果。
