NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search

Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed, which consists of repeatedly stacked basic novel cells with searchable attention operations. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better. This method can design high-performance and lightweight defect detection networks with data scarcity in industrial scenarios. The experimental results on four datasets demonstrate that the proposed method achieves superior performance and a relatively lighter model size compared to other competitive methods, including both manual and NAS-based approaches

Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models

Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal large models trained on public datasets often underperform in specific industrial domains. This paper proposes a multimodal federated learning framework that enables multiple enterprises to utilize private domain data to collaboratively train large models for vertical domains, achieving intelligent services across scenarios. The authors discuss in-depth the strategic transformation of federated learning in terms of intelligence foundation and objectives in the era of big model, as well as the new challenges faced in heterogeneous data, model aggregation, performance and cost trade-off, data privacy, and incentive mechanism. The paper elaborates a case study of leading enterprises contributing multimodal data and expert knowledge to city safety operation management , including distributed deployment and efficient coordination of the federated learning platform, technical innovations on data quality improvement based on large model capabilities and efficient joint fine-tuning approaches. Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management. The established federated learning cooperation ecosystem is expected to further aggregate industry, academia, and research resources, realize large models in multiple vertical domains, and promote the large-scale industrial application of artificial intelligence and cutting-edge research on multimodal federated learning

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by uneven illumination and improve the visual quality of such images. However, these methods suffer from the loss of defect-discriminative information and a high computational burden. To address the above issues, we propose a joint-prior-based uneven illumination enhancement (JPUIE) method. Specifically, a semi-coupled retinex model is first constructed to accurately and effectively eliminate uneven illumination. Furthermore, a multiscale Gaussian-difference-based background prior is proposed to reweight the data consistency term, thereby avoiding the loss of defect information in the enhanced image. Last, by using the powerful nonlinear fitting ability of deep neural networks, a deep denoised prior is proposed to replace existing physics priors, effectively reducing the time consumption. Various experiments are carried out on public and private datasets, which are used to compare the defect images and enhanced results in a symmetric way. The experimental results demonstrate that our method is more conducive to downstream visual inspection tasks than other methods

Investigation of the association between lens autofluorescence ratio and diabetes: a cross-sectional study.

Lens autofluorescence ratio (LFR) is a novel approach to detect advanced glycation end products in a time-saving and non-invasive manner. However, its associations with glycemia and diabetes remain unclear. We conducted this study to address this issue in Chinese adults. We enrolled a total of 4,705 participants aged 20-70 years in China between May 2020 and January 2021 in a cross-sectional study. LFR was determined by biomicroscopy (ClearPath DS-120). Diabetes was ascertained by oral glucose tolerance test, self-reported history, and/or antidiabetic medication use. Correlation and logistic regression analyses were performed. LFR was higher in participants with diabetes than those without (23.27 ± 6.51 vs. 19.45 ± 5.08, p < 0.001). LFR correlated with fasting plasma glucose and hemoglobin A1c in the overall and diabetes-stratified populations. The odds of diabetes was increased by 6% per one percent higher of LFR after multivariable-adjustment (odds ratio (OR) 1.06, 95% CI 1.04-1.08, p < 0.001). Participants in the highest quartile of LFR had higher odds of diabetes compared with those in the lowest quartile (OR 1.83, 95% CI 1.33-2.52, p < 0.001). Mediation analysis showed that, insulin resistance, as assessed by triglyceride-glucose index, may underline the relationship between high LFR and increased odds of diabetes. LFR, a non-invasive indirect measure of advanced glycation end products, appears to be associated with glycemia and the risk of developing diabetes in Chinese adults

Dark tea consumption is associated with a reduced risk of dysglycaemia and increased urinary glucose and sodium excretion in Chinese adults

Aim: To examine the associations of tea consumption (both frequency and type) with (1) prediabetes and diabetes and (2) urinary glucose and sodium excretion in Chinese community‐dwelling adults. Materials and Methods: In 1923 participants (457 with diabetes, 720 with prediabetes, and 746 with normoglycaemia), the frequency (occasional, frequent, daily, or nil) and type (green, black, dark, or other) of tea consumption were assessed using a standardized questionnaire. Morning spot urinary glucose and urine glucose‐to‐creatinine ratios (UGCRs) were assessed as markers of urinary glucose excretion. Tanaka's equation was used to estimate 24‐h urinary sodium excretion. Logistic and multivariate linear regression analyses were performed. Results: Compared with non‐tea drinkers, the corresponding multivariable‐adjusted odds ratios (ORs) for prediabetes and diabetes were 0.63 (95% confidence interval [CI] 0.48, 0.83) and 0.58 (95% CI 0.41, 0.82) in participants drinking tea daily. However, only drinking dark tea was associated with reduced ORs for prediabetes (0.49, 95% CI 0.36, 0.66) and diabetes (0.41, 95% CI 0.28, 0.62). Dark tea consumption was associated with increased morning spot urinary glucose (0.22 mmol/L, 95% CI 0.11, 0.34 mmol/L), UGCR (0.15 mmol/mmol, 95% CI 0.05, 0.25 mmol/L) and estimated 24‐h urinary sodium (7.78 mEq/day, 95% CI 2.27, 13.28 mEq/day). Conclusions: Regular tea consumption, especially dark tea, is associated with a reduced risk of dysglycaemia and increased urinary glucose and sodium excretion in Chinese community‐dwelling adults