Search Results (159)

Vehicle detection plays a pivotal role in traffic management as a key technology for intelligent traffic management and driverless driving. However, current deep learning-based vehicle detection models face several challenges in practical applications. These include slow detection speeds, large computational and parametric quantities, high leakage and misdetection rates in target-intensive environments, and difficulties in deploying them on edge devices with limited computing power and memory. To address these issues, this paper proposes an improved vehicle detection method called SMA-YOLO, based on the YOLOv7 model. Firstly, MobileNetV3 is adopted as the new backbone network to lighten the model. Secondly, the SimAM attention mechanism is incorporated to suppress background interference and enhance small-target detection capability. Additionally, the ACON activation function is substituted for the original SiLU activation function in the YOLOv7 model to improve detection accuracy. Lastly, SIoU is used to replace CIoU to optimize the loss of function and accelerate model convergence. Experiments on the UA-DETRAC dataset demonstrate that the proposed SMA-YOLO model achieves a lightweight effect, significantly reducing model size, computational requirements, and the number of parameters. It not only greatly improves detection speed but also maintains higher detection accuracy. This provides a feasible solution for deploying a vehicle detection model on embedded devices for real-time detection. Full article

Aiming at the key technology of defect detection in precision additive manufacturing of highly reflective metal materials, this study proposes an enhanced SCK-YOLOV5 framework, which combines polarization imaging and deep learning methods to significantly improve the intelligent identification ability of small metal micro and nano defects. This framework introduces the SNWD (Selective Network with attention for Defect and Weathering Degradation) Loss function, which combines the SIOU Angle Loss with the NWD distribution sensing characteristics. It is specially designed for automatic positioning and identification of micrometer hole defects. At the same time, we employ global space construction with a dual-attention mechanism and multi-scale feature refining technique with selection kernel convolution to extract multi-scale defect information from highly reflective surfaces stably. Combined with the polarization imaging preprocessing and the comparison of enhancement defects under high reflectivity, the experimental results show that the proposed method significantly improves the precision, recall rate, and mAP50 index compared with the YOLOv5 baseline (increased by 0.5%, 1.2%, and 1.8%, respectively). It is the first time that this improvement has been achieved among the existing methods based on the YOLO framework. It creates a new paradigm for intelligent defect detection in additive manufacturing of high-precision metal materials and provides more reliable technical support for quality control in industrial manufacturing. Full article

The widespread application of drone technology has raised security concerns, as unauthorized drones can lead to illegal intrusions and privacy breaches. Traditional detection methods often fall short in balancing performance and lightweight design, making them unsuitable for resource-constrained scenarios. To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. First, we design the CFE-AFPN network to streamline the architecture while boosting feature fusion capabilities across non-adjacent layers. Second, we introduce the SIoU loss function to address the orientation mismatch issue between predicted and ground truth bounding boxes. Finally, we employ the LAMP pruning algorithm to compress the model. Experimental results on the Anti-UAV dataset show that the improved model achieves a 2.9% increase in Precision, a 6.8% increase in Recall, and 3.9% and 3.8% improvements in mAP50 and mAP50-95, respectively. Additionally, the model size is reduced by 75%, the parameter count by 78%, and computational workload by 30%. Compared to mainstream algorithms, IASL-YOLO demonstrates significant advantages in both performance and lightweight design, offering an efficient solution for drone detection tasks. Full article

Pedestrian detection technology refers to identifying pedestrians within the field of view and is widely used in smart cities, public safety surveillance, and other scenarios. However, in real-world complex scenes, challenges such as high pedestrian density, occlusion, and low lighting conditions lead to blurred image boundaries, which significantly impact accuracy of pedestrian detection. To address these challenges, we propose a novel pedestrian detection algorithm, FA-YOLO. First, to address issues of limited effective information extraction in backbone network and insufficient feature map representation, we propose a feature enhancement module (FEM) that integrates both global and local features of the feature map, thereby enhancing the network’s feature representation capability. Then, to reduce redundant information and improve adaptability to complex scenes, an adaptive sparse self-attention (ASSA) module is designed to suppress noise interactions in irrelevant regions and eliminate feature redundancy across both spatial and channel dimensions. Finally, to further enhance the model’s focus on target features, we propose cross stage partial with adaptive sparse self-attention (C3ASSA), which improves overall detection performance by reinforcing the importance of target features during the final detection stage. Additionally, a scalable intersection over union (SIoU) loss function is introduced to address the vector angle differences between predicted and ground-truth bounding boxes. Extensive experiments on the WiderPerson and RTTS datasets demonstrate that FA-YOLO achieves State-of-the-Art performance, with a precision improvement of 3.5% on the WiderPerson and 3.0% on RTTS compared to YOLOv11. Full article

Copper and zinc are metals commonly used in industry. However, improperly disposed copper and zinc pollute soil seriously. In farmland where the concentrations of copper and zinc exceeded regulatory standards and farming has been banned for many years, we measured the copper and zinc concentrations in soil. The copper concentration ranged from 30.2 to 1082.3 mg/kg, while the zinc concentration was between 200.2 and 3335.3 mg/kg. To explore the correlation between the concentration of copper and zinc in soil and plants and plant growth, Pennisetum was chosen as the test crop. The economic and carbon reduction benefits of planting Pennisetum in copper- and zinc-polluted farmland were also investigated. The results indicated that the concentration levels of copper and zinc were not significantly impacted, and neither was the growth of Pennisetum. Farming Pennisetum produces a total of about 1100 tons of biomass per hectare per year. The income per hectare was about USD 48,000 per year. Pennisetum captures 578.8 tons of carbon every year, equivalent to 2124.2 ton-CO₂e. When used as fuel, it provides 23,649 GJ of bioenergy. Therefore, Pennisetum is an appropriate plant for the green and sustainable remediation of polluted soil. Full article

With the continuous growth of maritime activities and the shipping trade, the application of maritime target detection in remote sensing images has become increasingly important. However, existing detection methods face numerous challenges, such as small target localization, recognition of targets with large aspect ratios, and high computational demands. In this paper, we propose an improved target detection model, named YOLOv5-ASC, to address the challenges in maritime target detection. The proposed YOLOv5-ASC integrates three core components: an Attention-based Receptive Field Enhancement Module (ARFEM), an optimized SIoU loss function, and a Deformable Convolution Module (C3DCN). These components work together to enhance the model’s performance in detecting complex maritime targets by improving its ability to capture multi-scale features, optimize the localization process, and adapt to the large aspect ratios typical of maritime objects. Experimental results show that, compared to the original YOLOv5 model, YOLOv5-ASC achieves a 4.36 percentage point increase in mAP@0.5 and a 9.87 percentage point improvement in precision, while maintaining computational complexity within a reasonable range. The proposed method not only achieves significant performance improvements on the ShipRSImageNet dataset but also demonstrates strong potential for application in complex maritime remote sensing scenarios. Full article

Conventional inspections in car damage assessments depend on visual judgments by human inspectors, which are labor-intensive and prone to fraudulent practices through manipulating damages. Recent advancements in artificial intelligence have given rise to a state-of-the-art object detection algorithm, the You Only Look Once algorithm (YOLO), that sets a new standard in smart and automated damage assessment. This study proposes an enhanced YOLOv9 network tailored to detect six types of car damage. The enhancements include the convolutional block attention module (CBAM), applied to the backbone layer to enhance the model’s ability to focus on key damaged regions, and the SCYLLA-IoU (SIoU) loss function, introduced for bounding box regression. To be able to assess the damage severity comprehensively, we propose a novel formula named damage severity index (DSI) for quantifying damage severity directly from images, integrating multiple factors such as the number of detected damages, the ratio of damage to the image size, object detection confidence, and the type of damage. Experimental results on the CarDD dataset show that the proposed model outperforms state-of-the-art YOLO algorithms by 1.75% and that the proposed DSI demonstrates intuitive assessment of damage severity with numbers, aiding repair decisions. Full article

The accurate detection and identification of pests and diseases on cucumber leaves is a prerequisite for scientifically controlling such issues. To address the limited detection accuracy of existing models in complex and diverse natural backgrounds, this study proposes an improved deep learning network model based on YOLOv8, named SEDCN-YOLOv8. First, the deformable convolution network DCNv2 (Deformable Convolution Network version 2) is introduced, replacing the original C2f module with an improved C2f_DCNv2 module in the backbone feature extraction network’s final C2f block. This enhances the model’s ability to recognize multi-scale, deformable leaf shapes and disease characteristics. Second, a Separated and Enhancement Attention Module (SEAM) is integrated to construct an improved detection head, Detect_SEAM, which strengthens the learning of critical features in pest and disease channels. This module also captures the relationship between occluded and non-occluded leaves, thereby improving the recognition of diseased leaves that are partially obscured. Finally, the original CIOU loss function of YOLOv8 is replaced with the Focaler-SIOU loss function. The experimental results demonstrate that the SEDCN-YOLOv8 network achieves a mean average precision (mAP) of 75.1% for mAP50 and 53.1% for mAP50-95 on a cucumber pest and disease dataset, representing improvements of 1.8 and 1.5 percentage points, respectively, over the original YOLOv8 model. The new model exhibits superior detection accuracy and generalization capabilities, with a model size of 6 MB and a detection speed of 400 frames per second, fully meeting the requirements for industrial deployment and real-time detection. Therefore, the SEDCN-YOLOv8 network model demonstrates broad applicability and can be effectively used in large-scale real-world scenarios for cucumber leaf pest and disease detection. Full article

Background/Objectives: Exposure to fine particulate matter (PM_2.5) causes significant respiratory and gastrointestinal health problems. In our prior research, we identified Lactobacillus acidophilus TW01 as a promising strain for mitigating oxidative damage, enhancing wound healing in intestinal epithelial cells, and protecting bronchial cells from cigarette smoke extract. Building upon these findings, this study examines the protective effects of this strain on lung damage induced by particulate matter (PM) through the gut–lung axis in mouse models. Methods: This study evaluated the protective effects of L. acidophilus TW01 against PM_2.5-induced lung injury using two in vivo mouse models (OVA sensitization combined with PM_2.5 exposure and DSS-induced colitis). Results: L. acidophilus TW01 exhibited significant protective effects in two in-vivo models, reducing pro-inflammatory cytokines (TNF-α, IL-6, and IL-5), modulating the immune response (IgG subtypes), and improving gut barrier integrity. Importantly, L. acidophilus TW01 increased the abundance of beneficial gut bacteria (Bifidobacterium and Lactobacillus). Conclusions: These findings highlight the significant protective/therapeutic potential of L. acidophilus TW01 in mitigating the adverse health effects of PM_2.5 exposure, emphasizing the interplay between the gut and lung microbiomes in overall health. The multi-faceted protective effects of this probiotic suggest a novel, multi-pronged therapeutic strategy for addressing the widespread health consequences of air pollution. Full article

The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related materials, the model was augmented by incorporating an additional layer dedicated to enhancing the detection of small targets, thereby improving the overall accuracy. Furthermore, an attention mechanism was incorporated into the backbone network to focus on the features of the detection targets, thereby improving the detection efficacy of the model. Simultaneously, for the introduction of the SIoU loss function, the angular vector between the bounding box regressions was utilized to define the loss function, thus improving the training efficiency of the model. Following these enhancements, a channel pruning technique was employed to streamline the network, which not only reduced the parameter count but also expedited the inference process, yielding a more compact model for non-tobacco-related material detection. The experimental results on the NTRM dataset indicate that the LRNTRM-YOLO model achieved a mean average precision (mAP) of 92.9%, surpassing the baseline model by a margin of 4.8%. Additionally, there was a 68.3% reduction in the parameters and a 15.9% decrease in floating-point operations compared to the baseline model. Comparative analysis with prominent models confirmed the superiority of the proposed model in terms of its lightweight architecture, high accuracy, and real-time capabilities, thereby offering an innovative and practical solution for detecting non-tobacco-related materials in the future. Full article

Hepatitis C virus (HCV) eradication is usually associated with dyslipidemia. Most studies in this field have focused on genotype-specific direct-acting antivirals (DAAs), with research on pangenotypic DAAs being limited. This study examined how two pangenotypic DAA regimens, glecaprevir/pibrentasvir (GLE/PIB) and sofosbuvir/velpatasvir (SOF/VEL), affect lipid profiles and insulin resistance after viral eradication in chronic HCV patients. A total of 100 patients (57 with GLE/PIB and 43 with SOF/VEL) treated between September 2020 and January 2022 were included in the retrospective analysis. This study found a significant increase in LDL and TC levels after treatment (p < 0.001), but no significant changes in triglycerides, high-density lipoprotein, HbA1C, or the Homeostatic Model Assessment of Insulin Resistance. According to a logistic regression analysis, higher baseline LDL or TC and lower baseline glucose are predictors of the degree of increase in LDL or TC following a sustained virological response. Both pangenotypic DAA regimens significantly impact lipid profiles, particularly LDL and TC, but not insulin resistance. This study emphasizes the need for more research into the long-term metabolic effects of DAAs. Full article

Conveyor belt deviation is one of the most common failures in belt conveyors. To address issues such as the high computational complexity, large number of parameters, long inference time, and difficulty in feature extraction of existing conveyor belt deviation detection models, we propose a GES-YOLO algorithm for detecting deviation in mining belt conveyors, based on an improved YOLOv8s model. The core of this algorithm is to enhance the model’s ability to extract features in complex scenarios, thereby improving the detection efficiency. Specifically, to improve real-time detection capabilities, we introduce the Groupwise Separable Convolution (GSConv) module. Additionally, by analyzing scene features, we remove the large object detection layer, which enhances the detection speed while maintaining the feature extraction capability. Furthermore, to strengthen feature perception under low-light conditions, we introduce the Efficient Multi-Scale Attention Mechanism (EMA), allowing the model to obtain more robust features. Finally, to improve the detection capability for small objects such as conveyor rollers, we introduce the Scaled Intersection over Union (SIoU) loss function, enabling the algorithm to sensitively detect rollers and provide a precise localization for deviation detection. The experimental results show that the GES-YOLO significantly improves the detection performance in complex environments such as high-noise and low-illumination conditions in coal mines. Compared to the baseline YOLOv8s model, GES-YOLO’s mAP@0.5 and mAP@0.5:0.95 increase by 1.5% and 2.3%, respectively, while the model’s parameter count and computational complexity decrease by 38.2% and 10.5%, respectively. The Frames Per Second (FPS) of the average detection speed reaches 63.62. This demonstrates that GES-YOLO achieves a good balance between detection accuracy and inference speed, with excellent accuracy, robustness, and industrial application potential. Full article

Background: Despite the widespread use of lipid-lowering agents, the risk of atherosclerotic cardiovascular disease (ASCVD) remains; this residual risk has been attributed to remnant cholesterol (RC) levels. However, the causal associations between RC levels and various atherosclerosis-related cardiometabolic and vascular risk factors for ASCVD remain unclear. Methods: Using genetic and biochemical data of 108,876 Taiwan Biobank study participants, follow-up data of 31,790 participants, and follow-up imaging data of 18,614 participants, we conducted a genome-wide association study, a Functional Mapping and Annotation analysis, and bidirectional Mendelian randomization analyses to identify the genetic determinants of RC levels and the causal associations between RC levels and various cardiometabolic and vascular risk factors. Results: We found that higher RC levels were associated with higher prevalence or incidence of the analyzed risk factors. The genome-wide association study unveiled 61 lead genetic variants determining RC levels. The Functional Mapping and Annotation analysis revealed 21 gene sets exhibiting strong enrichment signals associated with lipid metabolism. Standard Mendelian randomization models adjusted for nonlipid variables and low-density lipoprotein cholesterol levels unraveled forward causal associations of RC levels with the prevalence of diabetes mellitus, hypertension, microalbuminuria, and metabolic liver disease. Reverse Mendelian randomization analysis revealed the causal association of diabetes mellitus with RC levels. Conclusions: RC levels, mainly influenced by genes associated with lipid metabolism, exhibit causal associations with various cardiometabolic risk factors, including diabetes mellitus, hypertension, microalbuminuria, and metabolic liver disease. This study provides further insights into the role of RC levels in predicting the residual risk of ASCVD. Full article

Existing surface defect detection methods for micro-motor commutators suffer from low detection accuracy, poor real-time performance, and high false detection and missed detection rates for small targets. To address these issues, this paper proposes a high-performance and robust commutator surface defect detection model (CLS-YOLO), using YOLOv11-n as the baseline model. First, a lightweight Cross-Scale Feature Fusion Module (CCFM) is introduced to integrate features from different scales, enhancing the model’s adaptability to scale variations and ability to detect small objects. This approach reduces model parameters and improves detection speed without compromising detection accuracy. Second, a Large Separable Kernel Attention (LSKA) module is incorporated into the detection head to strengthen feature understanding and capture, reducing interference from complex surface patterns on the commutator and significantly improving adaptability to various target types. Finally, to address issues related to the center point location, aspect ratio, angle, and sample imbalance in bounding boxes, SIoU Loss replaces the CIoU Loss in the original network, overcoming limitations of the original loss function and enhancing overall detection performance. Model performance was evaluated and compared on a commutator surface defect detection dataset, with additional experiments designed to verify the model’s effectiveness and feasibility. Experimental results show that, compared to YOLOv11-n, the CLS-YOLO model achieves a 2.08% improvement in mAP@0.5. This demonstrates that CLS-YOLO can accurately detect large defect targets while maintaining accuracy for tiny defects. Additionally, CLS-YOLO outperforms most YOLO-series models, including YOLOv8-n and YOLOv10-n. The model’s parameter count is only 1.860 million, lower than YOLOv11-n, with a detection speed increase of 8.34%, making it suitable for deployment on resource-limited terminal devices in complex industrial scenarios. Full article

To address challenges such as the frequent misdetection of targets, missed detections of multiple targets, high computational demands, and poor real-time detection performance in the video surveillance of external breakage obstacles on transmission lines, we propose a lightweight target detection algorithm incorporating the ACmix mechanism. First, the ShuffleNetv2 backbone network is used to reduce the model parameters and improve the detection speed. Next, the ACmix attention mechanism is integrated into the Neck layer to suppress irrelevant information, mitigate the impact of complex backgrounds on feature extraction, and enhance the network’s ability to detect small external breakage targets. Additionally, we introduce the PC-ELAN module to replace the ELAN-W module, reducing redundant feature extraction in the Neck network, lowering the model parameters, and boosting the detection efficiency. Finally, we adopt the SIoU loss function for bounding box regression, which enhances the model stability and convergence speed due to its smoothing characteristics. The experimental results show that the proposed algorithm achieves an mAP of 92.7%, which is 3% higher than the baseline network. The number of model parameters and the computational complexity are reduced by 32.3% and 44.9%, respectively, while the detection speed is improved by 3.5%. These results demonstrate that the proposed method significantly enhances the detection performance. Full article