This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Objective:This study aimed to investigate the prevalence,causes,and influencing factors of vision impairment in the elderly population aged 60 years and above in Mangxin Town,Kashgar region,Xinjiang,China.Located in a...Objective:This study aimed to investigate the prevalence,causes,and influencing factors of vision impairment in the elderly population aged 60 years and above in Mangxin Town,Kashgar region,Xinjiang,China.Located in a region characterized by intense ultraviolet radiation and arid climatic conditions,Mangxin Town presents unique environmental challenges that may exacerbate ocular health issues.Despite the global emphasis on addressing vision impairment among aging populations,there remains a paucity of updated and region-specific data in Xinjiang,necessitating this comprehensive assessment to inform targeted interventions.Methods:A cross-sectional study was conducted from May to June 2024,involving 1,311 elderly participants(76.76%participation rate)out of a total eligible population of 1,708 individuals aged≥60 years.Participants underwent detailed ocular examinations,including assessments of uncorrected visual acuity(UVA)and best-corrected visual acuity(BCVA)using standard logarithmic charts,slit-lamp biomicroscopy,optical coherence tomography(OCT,Topcon DRI OCT Triton),fundus photography,and intraocular pressure measurement(Canon TX-20 Tonometer).A multidisciplinary team of 10 ophthalmologists and 2 local village doctors,trained rigorously in standardized protocols,ensured consistent data collection.Demographic,lifestyle,and medical history data were collected via questionnaires.Statistical analyses,performed using STATA 16,included multivariate logistic regression to identify risk factors,with significance defined as P<0.05.Results:The overall prevalence of vision impairment was 13.21%(95%CI:11.37%-15.04%),with low vision at 11.76%(95%CI:10.01%-13.50%)and blindness at 1.45%(95%CI:0.80%-2.10%).Cataract emerged as the leading cause,responsible for 68.20%of cases,followed by glaucoma(5.80%),optic atrophy(5.20%),and age-related macular degeneration(2.90%).Vision impairment prevalence escalated significantly with age:7.74%in the 60–69 age group,17.79%in 70–79,and 33.72%in those≥80.Males exhibited higher prevalence than females(15.84%vs.10.45%,P=0.004).Multivariate analysis revealed age≥80 years(OR=6.43,95%CI:3.79%-10.90%),male sex(OR=0.53,95%CI:0.34%-0.83%),and daily exercise(OR=0.44,95%CI:0.20%-0.95%)as significant factors.History of eye disease showed a non-significant trend toward increased risk(OR=1.49,P=0.107).Education level,income,and smoking status showed no significant associations.Conclusions:This study underscores cataract as the predominant cause of vision impairment in Mangxin Town’s elderly population,with age and sex as critical determinants.The findings align with global patterns but highlight region-specific challenges,such as environmental factors contributing to cataract prevalence.Public health strategies should prioritize improving access to cataract surgery,enhancing grassroots ophthalmic infrastructure,and integrating portable screening technologies for early detection of fundus diseases.Additionally,promoting health education on UV protection and lifestyle modifications,such as regular exercise,may mitigate risks.Future research should expand to broader regions in Xinjiang,employ advanced diagnostic tools for complex conditions like glaucoma,and explore longitudinal trends to refine intervention strategies.These efforts are vital to reducing preventable blindness and improving quality of life for aging populations in underserved areas.展开更多
Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness...Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness are easily affected by limited computing power of airborne equipment,complex aerial scenes and partial occlusion.To address the above challenges,we propose a novel drogue keypoint detection and pose measurement algorithm based on monocular vision,and realize real-time processing on airborne embedded devices.Firstly,a lightweight network is designed with structural re-parameterization to reduce computational cost and improve inference speed.And a sub-pixel level keypoints prediction head and loss functions are adopted to improve keypoint detection accuracy.Secondly,a closed-form solution of drogue pose is computed based on double spatial circles,followed by a nonlinear refinement based on Levenberg-Marquardt optimization.Both virtual simulation and physical simulation experiments have been used to test the proposed method.In the virtual simulation,the mean pixel error of the proposed method is 0.787 pixels,which is significantly superior to that of other methods.In the physical simulation,the mean relative measurement error is 0.788%,and the mean processing time is 13.65 ms on embedded devices.展开更多
China’s five-year plans crystallize a governance model that merges long-term strategic vision with adaptive execution.AS China prepares to unveil its 15th Five-Year Plan in 2026,policymakers,investors,and scholars ar...China’s five-year plans crystallize a governance model that merges long-term strategic vision with adaptive execution.AS China prepares to unveil its 15th Five-Year Plan in 2026,policymakers,investors,and scholars around the world are watching closely.For over 70 years,these plans have guided the country’s economic and social development.展开更多
Blindness affected 45 million people globally in 2021,and moderate to severe vision loss a further 295 million.[1]The most common causes,cataract and uncorrected refractive error,are generally the easiest to treat,and...Blindness affected 45 million people globally in 2021,and moderate to severe vision loss a further 295 million.[1]The most common causes,cataract and uncorrected refractive error,are generally the easiest to treat,and are among the most cost-effective procedures in all of medicine and international development.[1-2]Thus,vision impairment is both extremely common and,in principle,readily manageable.展开更多
Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances a...Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.展开更多
Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden ...Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.展开更多
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
基金supported by Science and Technology Planning Project of Guangzhou City(2024A04J4474).
文摘Objective:This study aimed to investigate the prevalence,causes,and influencing factors of vision impairment in the elderly population aged 60 years and above in Mangxin Town,Kashgar region,Xinjiang,China.Located in a region characterized by intense ultraviolet radiation and arid climatic conditions,Mangxin Town presents unique environmental challenges that may exacerbate ocular health issues.Despite the global emphasis on addressing vision impairment among aging populations,there remains a paucity of updated and region-specific data in Xinjiang,necessitating this comprehensive assessment to inform targeted interventions.Methods:A cross-sectional study was conducted from May to June 2024,involving 1,311 elderly participants(76.76%participation rate)out of a total eligible population of 1,708 individuals aged≥60 years.Participants underwent detailed ocular examinations,including assessments of uncorrected visual acuity(UVA)and best-corrected visual acuity(BCVA)using standard logarithmic charts,slit-lamp biomicroscopy,optical coherence tomography(OCT,Topcon DRI OCT Triton),fundus photography,and intraocular pressure measurement(Canon TX-20 Tonometer).A multidisciplinary team of 10 ophthalmologists and 2 local village doctors,trained rigorously in standardized protocols,ensured consistent data collection.Demographic,lifestyle,and medical history data were collected via questionnaires.Statistical analyses,performed using STATA 16,included multivariate logistic regression to identify risk factors,with significance defined as P<0.05.Results:The overall prevalence of vision impairment was 13.21%(95%CI:11.37%-15.04%),with low vision at 11.76%(95%CI:10.01%-13.50%)and blindness at 1.45%(95%CI:0.80%-2.10%).Cataract emerged as the leading cause,responsible for 68.20%of cases,followed by glaucoma(5.80%),optic atrophy(5.20%),and age-related macular degeneration(2.90%).Vision impairment prevalence escalated significantly with age:7.74%in the 60–69 age group,17.79%in 70–79,and 33.72%in those≥80.Males exhibited higher prevalence than females(15.84%vs.10.45%,P=0.004).Multivariate analysis revealed age≥80 years(OR=6.43,95%CI:3.79%-10.90%),male sex(OR=0.53,95%CI:0.34%-0.83%),and daily exercise(OR=0.44,95%CI:0.20%-0.95%)as significant factors.History of eye disease showed a non-significant trend toward increased risk(OR=1.49,P=0.107).Education level,income,and smoking status showed no significant associations.Conclusions:This study underscores cataract as the predominant cause of vision impairment in Mangxin Town’s elderly population,with age and sex as critical determinants.The findings align with global patterns but highlight region-specific challenges,such as environmental factors contributing to cataract prevalence.Public health strategies should prioritize improving access to cataract surgery,enhancing grassroots ophthalmic infrastructure,and integrating portable screening technologies for early detection of fundus diseases.Additionally,promoting health education on UV protection and lifestyle modifications,such as regular exercise,may mitigate risks.Future research should expand to broader regions in Xinjiang,employ advanced diagnostic tools for complex conditions like glaucoma,and explore longitudinal trends to refine intervention strategies.These efforts are vital to reducing preventable blindness and improving quality of life for aging populations in underserved areas.
基金supported by the National Science Fund for Distinguished Young Scholars,China(No.51625501)Aeronautical Science Foundation of China(No.20240046051002)National Natural Science Foundation of China(No.52005028).
文摘Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness are easily affected by limited computing power of airborne equipment,complex aerial scenes and partial occlusion.To address the above challenges,we propose a novel drogue keypoint detection and pose measurement algorithm based on monocular vision,and realize real-time processing on airborne embedded devices.Firstly,a lightweight network is designed with structural re-parameterization to reduce computational cost and improve inference speed.And a sub-pixel level keypoints prediction head and loss functions are adopted to improve keypoint detection accuracy.Secondly,a closed-form solution of drogue pose is computed based on double spatial circles,followed by a nonlinear refinement based on Levenberg-Marquardt optimization.Both virtual simulation and physical simulation experiments have been used to test the proposed method.In the virtual simulation,the mean pixel error of the proposed method is 0.787 pixels,which is significantly superior to that of other methods.In the physical simulation,the mean relative measurement error is 0.788%,and the mean processing time is 13.65 ms on embedded devices.
文摘China’s five-year plans crystallize a governance model that merges long-term strategic vision with adaptive execution.AS China prepares to unveil its 15th Five-Year Plan in 2026,policymakers,investors,and scholars around the world are watching closely.For over 70 years,these plans have guided the country’s economic and social development.
文摘Blindness affected 45 million people globally in 2021,and moderate to severe vision loss a further 295 million.[1]The most common causes,cataract and uncorrected refractive error,are generally the easiest to treat,and are among the most cost-effective procedures in all of medicine and international development.[1-2]Thus,vision impairment is both extremely common and,in principle,readily manageable.
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金the National Key Research and Development Program of China(2021YFA0717900)National Natural Science Foundation of China(62471251,62405144,62288102,22275098,and 62174089)+1 种基金Basic Research Program of Jiangsu(BK20240033,BK20243057)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB402).
文摘Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ30817)Hunan Provincial Natural Science Foundation-Hengyang City Joint Fund Project(2025JJ70129)+1 种基金Changsha Natural Science Foundation(kq2403057)China。
文摘Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.
