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
The changes in cotton leaf characteristics are closely related to the cotton spider mites’damage level.Extracting the distinguishable features of cotton leaves is an effective method to identify the level.However,it ...The changes in cotton leaf characteristics are closely related to the cotton spider mites’damage level.Extracting the distinguishable features of cotton leaves is an effective method to identify the level.However,it faces enormous challenges for the classification due to various factors,such as illumination intensity,background complexity,shooting angle and so on.A recognition model is proposed,which is trained through transfer learning with the two-stage learning rate from 0.01 to 0.001 based on MobileNetV1.The experiments demonstrate that the deep learning model attains the accuracy of 92.29%for the training set and 91.88%for the test set of the mixed data.For testifying the effectiveness of the two-stage training method,the models are trained with the two public datasets,CIFAR-10 and Flowers,and attain the accuracy of 95.46%and 95.57%for the test sets,respectively.The average recognition time for a single cotton leaf image is about 0.015 s.Furthermore,the mobile terminal application is developed with the model embedded,to realize the real-time recognition for cotton spider mites’damage level in the field.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。
基金Thanks for the support of National Key Research and Development Program of China(No.2016YFB0501805)2017 New Mode Application Project of Intelligent Manufacturing(New Mode Application of Remote Operation and Maintenance Service for Modern Agricultural Machinery Equipment).
文摘The changes in cotton leaf characteristics are closely related to the cotton spider mites’damage level.Extracting the distinguishable features of cotton leaves is an effective method to identify the level.However,it faces enormous challenges for the classification due to various factors,such as illumination intensity,background complexity,shooting angle and so on.A recognition model is proposed,which is trained through transfer learning with the two-stage learning rate from 0.01 to 0.001 based on MobileNetV1.The experiments demonstrate that the deep learning model attains the accuracy of 92.29%for the training set and 91.88%for the test set of the mixed data.For testifying the effectiveness of the two-stage training method,the models are trained with the two public datasets,CIFAR-10 and Flowers,and attain the accuracy of 95.46%and 95.57%for the test sets,respectively.The average recognition time for a single cotton leaf image is about 0.015 s.Furthermore,the mobile terminal application is developed with the model embedded,to realize the real-time recognition for cotton spider mites’damage level in the field.
