Search Results (16,951)

With the miniaturization and increasing complexity of electronic devices, the accuracy and efficiency of printed circuit board (PCB) defect detection are crucial to ensuring product quality. To address the issues of small defect sizes and high missed detection rates in PCB surface inspection, this paper proposes an enhanced YOLOv8s model which not only improves detection performance but also achieves a lightweight design. Firstly, the Nexus Attention module is introduced, which organically integrates multiple attention mechanisms to further enhance feature extraction and fusion capabilities, improving the model’s learning and generalization performance. Secondly, an improved CGFPN network is designed to optimize multi-scale feature fusion, significantly boosting the detection of small objects. Additionally, the WaveletUnPool module is incorporated, leveraging wavelet transform technology to refine the upsampling process, accurately restoring detailed information and improving small-object detection in complex backgrounds. Lastly, the C2f-GDConv module replaces the traditional C2f module, reducing the number of model parameters and computational complexity while maintaining feature extraction efficiency. Comparative experiments on a public PCB dataset demonstrate that the enhanced model achieved a mean average precision (mAP) of 97.3% in PCB defect detection tasks, representing a 3.0% improvement over the original model, while reducing Giga Floating Point Operations (GFLOPs) by 26.8%. These enhancements make the model more practical and adaptable for industrial applications, providing a solid foundation for future research. Full article

An approach to detecting discontinuities in carbon fiber-reinforced polymers, caused by impact loading followed by compression testing, was developed. An X-ray sensor-based installation was used, while some algorithms were developed to improve the quality of the obtained low-contrast radiographic images with negligible signal-to-noise ratios. For epoxy/AF (#1) composite subjected to a “high-velocity” steel-ball impact with subsequent compression loading, it was not possible to detect discontinuities since the orientation of the extended zone of interlayer delamination was perpendicular to the irradiation axis. After drop-weight impacts with subsequent compression loading of epoxy/CF (#2) and PEEK/CF (#3) composites, the main cracks were formed in their central parts. This area was reliably detected through the improved radiographic images being more contrasted compared to that for composite #3, for which the damaged area was similar in shape but smaller. The phase variation and congruency methods were employed to highlight low-contrast objects in the radiographic images. The phase variation procedure showed higher efficiency in detecting small objects, while phase congruency is preferable for highlighting large objects. To assess the degree of image improvement, several metrics were implemented. In the analysis of the model images, the most indicative was the PSNR parameter (with a S-N ratio greater than the unit), confirming an increase in image contrast and a decrease in noise level. The NIQE and PIQE parameters enabled the correct assessment of image quality even with the S-N ratio being less than a unit. Full article

This study focuses on assessing the indoor air quality in a storage room (SR) belonging to Museo Nazionale Scienza e Tecnologia Leonardo da Vinci in Milan (MUST), covering pollutants originating from outdoor sources and emissions from historical plastic objects made from cellulose acetate (CA), cellulose nitrate (CN), and urea–formaldehyde (UF) stored in metal cabinets. The concentrations of SO₂ (sulphur dioxide), NO₂ (nitrogen dioxide), NOx (nitrogen oxides), HONO (nitrous acid), HNO₃ (nitric acid), O₃ (ozone), NH₃ (ammonia), CH₃COOH (acetic acid), and HCOOH (formic acid) were determined. The concentrations of SO₂, O₃, and NOx measured inside the metal cabinets were consistently lower compared to the other sampling sites. This result was expected due to their reactivity and the lack of internal sources. The SR and metal cabinets showed similar concentrations of NO and NO₂, except for CA, where a high NO concentration was detected. The interaction between the CA surfaces and NO₂ altered the distribution of NO and NO₂, leading to a significant increase in NO. The presence of HNO₃ potentially led to the formation of ammonium nitrate, as confirmed by ER-FTIR measurements. High levels of HONO and HNO₃ in CN and NH₃ in the UF indicate object deterioration, while elevated concentrations of CH₃COOH in CA and HCOOH in the SR suggest specific degradation pathways for cellulose acetate and other organic materials, respectively. These results could direct conservators towards the most appropriate practical actions. Full article

Background/Objectives: This study aimed to assess the incidence of diabetic retinopathy (DR) in patients with type 2 diabetes (T2DM) treated in primary-care settings in Catalonia, Spain, and identify key risk factors associated with DR development. Methods: A retrospective cohort study was conducted using the SIDIAP (System for Research and Development in Primary Care) database. Patients aged 30–90 with T2DM who underwent retinal screening between 2010 and 2015 were included. Multivariable Cox regression analysis was used to assess the impact of clinical variables, including HbA1c levels, diabetes duration, and comorbidities, on DR incidence. Results: This study included 146,506 patients, with a mean follow-up time of 6.96 years. During this period, 4.7% of the patients developed DR, resulting in an incidence rate of 6.99 per 1000 person-years. Higher HbA1c levels were strongly associated with an increased DR risk, with patients with HbA1c > 10% having more than four times the risk compared to those with HbA1c levels < 7% (hazard ratio: 4.23; 95% CI: 3.90–4.58). Other significant risk factors for DR included greater diabetes duration, male sex, ex-smoker status, macrovascular disease, and chronic kidney disease. In contrast, obesity appeared to be a protective factor against DR, with an HR of 0.93 (95% CI: 0.89–0.98). Conclusions: In our real-world setting, the incidence rate of DR was 6.99 per 1000 person-years. Poor glycemic control, especially HbA1c < 10%, and prolonged diabetes duration were key risk factors. Effective management of these factors is crucial in preventing DR progression. Regular retinal screenings in primary care play a vital role in early detection and reducing the DR burden for T2DM patients. Full article

Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection process. Moreover, operations like downsampling during feature extraction can cause a significant loss of spatial information for small objects, adversely affecting detection accuracy. To tackle these issues, we propose ESL-YOLO, which incorporates feature enhancement, fusion, and a local attention pyramid. This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. Evaluations on the publicly accessible remote sensing datasets AI-TOD and DOTAv1.5 indicate that ESL-YOLO significantly surpasses other contemporary object detection frameworks. In particular, ESL-YOLO enhances mean average precision mAP by 10% and 1.1% on the AI-TOD and DOTAv1.5 datasets, respectively, compared to YOLOv8s. This model is particularly adept at small object detection in remote sensing imagery and holds significant potential for practical applications. Full article

Background/Objectives:The emergence of monkeypox outside its endemic region in Africa has raised significant concerns within the public health community due to its rapid global dissemination. Early clinical differentiation of monkeypox from similar diseases, such as chickenpox and measles, presents a challenge. The Monkeypox Skin Lesion Dataset (MSLD) used in this study comprises monkeypox skin lesions, which were collected primarily from publicly accessible sources. The dataset contains 770 original images captured from 162 unique patients. The MSLD includes four distinct class labels: monkeypox, measles, chickenpox, and normal. Methods: This paper presents an ensemble model for classifying the monkeypox dataset, which includes transformer models and support vector machine (SVM). The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. The proposed model is developed by selecting the top four models based on evaluation metrics for performance. The top four CNN architectures, namely EfficientNetB0, ResNet50, MobileNet, and Xception, are used for feature extraction. The high-dimensional feature vectors extracted from each network are then concatenated and optimized before being inputted into the SVM classifier. Results: The proposed ensemble model, in conjunction with the SVM classifier, achieves an accuracy of 95.45b%. Furthermore, the model demonstrates high precision (95.51%), recall (95.45%), and F1 score (95.46%), indicating its effectiveness in identifying monkeypox lesions. Conclusions: The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. The model’s high diagnostic accuracy and computational efficiency indicate that it can be used as an additional tool for clinical decision support. Full article

This study presents a novel video-based risk assessment and safety management technique aimed at mitigating the risk of falling objects during tower crane lifting operations. The conventional YOLOv5 algorithm is prone to issues of missed and false detections, particularly when identifying small objects. To address these limitations, the algorithm is enhanced by incorporating an additional small object detection layer, implementing an attention mechanism, and modifying the loss function. The enhanced YOLOv5s model achieved precision and recall rates of 96.00%, with average precision (AP) values of 96.42% at an IoU of 0.5 and 62.02% across the range of IoU values from 0.5 to 0.95. These improvements significantly enhance the model’s capability to accurately detect crane hooks and personnel. Upon identifying the hook within a video frame, its actual height is calculated using an interpolation function derived from the hook’s dimensions. This calculation allows for the precise demarcation of the danger zone by determining the potential impact area of falling objects. The worker’s risk level is assessed using a refined method based on the statistical analysis of past accidents. If the risk level surpasses a predetermined safety threshold, the worker’s detection box is emphasized and flagged as a caution on the monitoring display. Full article

Remote sensing images play a vital role in domains including environmental monitoring, agriculture, and autonomous driving. However, the detection of targets in remote sensing images remains a challenging task. This study introduces innovative methods to enhance feature extraction, feature fusion, and model optimization. The Adaptive Selective Feature Enhancement Module (AFEM) dynamically adjusts feature weights using GhostModule and sigmoid functions, thereby enhancing the accuracy of small target detection. Moreover, the Adaptive Multi-scale Convolution Kernel Feature Fusion Module (AKSFFM) enhances feature fusion through multi-scale convolution operations and attention weight learning mechanisms. Moreover, our proposed ARSOD-YOLO optimized the network architecture, component modules, and loss functions based on YOLOv8, enhancing outstanding small target detection capabilities while preserving model efficiency. We conducted experiments on the VEDAI and AI-TOD datasets, showcasing the excellent performance of ARSOD-YOLO. Our algorithm achieved an mAP50 of 74.3% on the VEDAI dataset, surpassing the YOLOv8 baseline by 3.1%. Similarly, on the AI-TOD dataset, the mAP50 reached 47.8%, exceeding the baseline network by 6.1%. Full article

Background/Objectives: Bladder cancer is a type of cancer that begins in the cells lining the inner surface of the bladder. Although it usually begins in the bladder, it can spread to surrounding tissues, lymph nodes, and other organs in later stages. Early detection of bladder cancer is, therefore, of great importance. Methods: Therefore, this study developed two systems based on classification and Content-Based Image Retrieval (CBIR). The primary purpose of CBIR systems is to compare the visual similarities of a user-provided image with the images in the database and return the most similar ones. CBIR systems offer an effective search and retrieval mechanism by directly using the content of the image data. Results: In the proposed CBIR system, five different CNNs, two different textural-based feature extraction methods, and seven different similarity measurement metrics were tested for feature selection and similarity measurement. Successful feature extraction methods and similarity measurement metrics formed the infrastructure of the developed system. Densenet201 was preferred for feature extraction in the developed system. The cosine metric was used in the proposed CBIR system as a similarity measurement metric, the most successful among seven different metrics. Conclusions: As a result, it was seen that the proposed CBIR model showed the highest success using the Densenet201 model for feature extraction and the Cosine similarity measurement method. Full article

Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images. Methods: We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. Results: The findings show that background image proportions greatly influenced model inference. Moreover, schemes incorporating binary classification consistently improved performance, and CNN-based models outperformed Transformer-based models across all scenarios. Conclusions: We have developed a more efficient and reliable system for the automated detection of disease labels and location bounding boxes in CXR images. Full article

This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases. To ensure the model’s robustness and generalizability beyond the training dataset, it was further tested on a set of unseen images sourced from Google Images. This additional testing aimed to assess the model’s effectiveness in real-world scenarios, where it might encounter new data. The evaluation results were auspicious, demonstrating the model’s capability to classify plant conditions, such as diseases, with high accuracy. Moreover, the use of YOLOv8 offers significant improvements in speed and precision, making it suitable for real-time plant disease monitoring applications. The findings highlight the potential of this methodology for broader agricultural applications, including early disease detection and prevention. Full article

Background/Objectives: Sarcopenic obesity adversely affects physical function and activities of daily living (ADL) in older individuals and patients undergoing rehabilitation. This condition is also common in individuals with spinal cord injury (SCI); however, its relationship with ADL in this group remains unclear. Hence, this study examined the association between sarcopenic obesity and ADL in individuals with SCI. Methods: This retrospective cross-sectional study identified sarcopenia using the low skeletal muscle mass index (SMI) and Asian Working Group for Sarcopenia reference values. Obesity was defined as a body fat percentage (%BF) exceeding 25% in men and 35% in women. Sarcopenic obesity was identified when both the sarcopenia and obesity criteria were met. The primary outcome, ADL, was measured using the Functional Independence Measure (FIM). Multiple linear regression models were used to analyze the associations among the SMI, %BF, and FIM scores, after adjusting for age, sex, lesion level, injury severity, comorbidities, and injury duration. Results: Of 82 participants (median age: 63.5 years; 18.3% women), 62.2% had sarcopenic obesity. Participants with sarcopenic obesity (54 vs. 69 points, p = 0.006) had significantly lower FIM motor scores than those without this condition. Multiple linear regression analysis revealed that SMI (β = 0.416, p < 0.001) and %BF (β = −0.325, p = 0.009) were independently associated with the FIM motor scores. Conclusions: Decreased SMI and increased %BF in patients with SCI were independently associated with decreased ADL independence. Routine body composition assessments are necessary for early detection and intervention in this population. Full article

The Lens Dysfunction Syndrome includes two widespread ocular disorders: presbyopia and cataract. Understanding its etiology, onset, progression, impact, prevention, and treatment remains a significant scientific challenge. The lens is a fundamental structure of the ocular dioptric system that allows for focus adjustment or accommodation to view objects at different distances. Its opacification, primarily related to aging, leads to the development of cataracts. Traditionally, lens alterations have been diagnosed using a slit lamp and later with devices based on the Scheimpflug camera. However, both methods have significant limitations. In recent years, optical coherence tomography (OCT) has become a valuable tool for assessing the lens and pseudophakic intraocular lenses (IOLs) in clinical practice, providing a highly detailed non-invasive evaluation of these structures. Its clinical utility has been described in assessing the shape, location or position, and size of the lens, as well as in determining the degree and type of cataract and its various components. Regarding pseudophakic IOLs, OCT allows for the accurate assessment of their position and centering, as well as for detecting possible complications, including the presence of glistening or IOL opacification. Furthermore, OCT enables the evaluation of the posterior capsule and its associated pathologies, including late capsular distension syndrome. This review highlights the key applications of OCT in the assessment of the lens and pseudophakic IOLs. Full article

Lipopolysaccharide (LPS) is one of the main factors inducing endometritis in dairy cows. However, the specific pathogenesis of LPS-induced endometritis in dairy cows is not fully understood. The objective of this study was to establish an in vitro endometritis model using LPS-induced bovine endometrial epithelial (BEND) cells. BEND cells were treated with LPS of different concentrations and times. The cell-counting kit-8 (CCK-8) was used to detect the cell survival rate after LPS treatment, and quantitative real-time PCR (RT-qPCR) was used to detect the expression of control group and LPS-treated group of inflammatory factors interleukin-1 beta (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α). The results showed that the survival rate of endometrial epithelial cells stimulated by 5 μg/mL LPS for 6 h was 75.13%, and the expression of inflammatory factors was significantly increased. Therefore, 5 μg/mL LPS for 6 h could be selected as a suitable model for the study of inflammation. In addition, miRNA sequencing and target gene prediction was performed on normal and LPS-treated BEND cells. Among twenty-one differentially expressed miRNAs, six miRNAs were selected and their expression levels were detected by RT-qPCR, which were consistent with the sequencing results. Twenty-one differentially expressed miRNAs collectively predicted 17,050 target genes. This study provides a theoretical basis for further investigation of the pathogenesis of endometritis. Full article

In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due to the small size, dense distribution, and abundance of low-quality signature marks. To tackle these challenges, we introduce the Book Signature Marks Detection (BSMD-YOLOv8) model, specifically designed for scenarios involving small, closely spaced objects such as signature marks. Our proposed backbone, the Lightweight Multi-scale Residual Network (LMRNet), achieves a lightweight network while enhancing the accuracy of small object detection. To address the issue of insufficient fusion of local and global feature information in PANet, we design the Low-stage gather-and-distribute (Low-GD) module and the High-stage gather-and-distribute (High-GD) module to enhance the model’s multi-scale feature fusion capabilities, thereby refining the integration of local and global features of signature marks. Furthermore, we introduce Wise-IoU (WIoU) as a replacement for CIoU, prioritizing anchor boxes with moderate quality and mitigating harmful gradients from low-quality examples. Experimental results demonstrate that, compared to YOLOv8n, BSMD-YOLOv8 reduces the number of parameters by 65%, increases the frame rate by 7 FPS, and enhances accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively, achieving rapid and accurate detection of signature marks. Full article