High Detection Accuracy Research Articles

The revolutionary impact of ai in the automotive industry

The automotive industry is undergoing a revolutionary transformation through the integration of Artificial Intelligence (AI) technologies. This article examines the multifaceted impact of AI across manufacturing, autonomous vehicle development, smart factory solutions, and customer experience enhancement. The article highlights significant improvements in manufacturing efficiency, with AI-driven quality control systems achieving high accuracy in defect detection and reducing production costs substantially. In autonomous vehicle development, AI systems have demonstrated remarkable capabilities, with object detection accuracy reaching exceptional levels under optimal conditions and decision-making architectures processing multiple discrete decisions per second. The implementation of AI in smart factories has led to considerable improvement in overall equipment effectiveness, while customer experience enhancements have resulted in a significant increase in satisfaction scores. The integration of AI in supply chain management and market intelligence has yielded substantial operational improvements, with demand forecasting accuracy reaching impressive levels for short-term predictions and enabling meaningful reduction in inventory costs.

A Survey on Liver Cancer Detection Using Hyperfusion of CNN and SVM in Machine Learning

Since liver cancer ranks among of the most aggressive renditions of the disease, improving patient outcomes requires early identification. We propose an inventive tactic to liver cancer detection by integrating CNN and SVM. CNNs, known for their powerful feature extraction capabilities, are particularly effective in analysing complex medical images. SVMs, on the other hand, are efficient classifiers that can separate data points in high-dimensional spaces with accuracy. By merging the feature extraction strength of CNN with the classification efficiency of SVM, the proposed model aims to enhance liver cancer detection accuracy and robustness. The experimental results reveal that the fused CNN-SVM model significantly surpasses the performance of standalone CNN and SVM models, achieving a high detection accuracy of 95.2%. This hybrid method offers a promising direction for improving the precision of computer-aided diagnosis systems, contributing to more effective and reliable liver cancer detection methods that can assist healthcare professionals in making timely decisions.

Vision-based manipulation of transparent plastic bags in industrial setups

IntroductionThis paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, contributing to the Industry 4.0 paradigm. Industry 4.0, emphasizing data-driven processes, connectivity, and robotics, enhances accessibility and sustainability across the value chain. Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. Tracking algorithms and depth-sensing technologies are integrated to enable 3D spatial awareness during pick-and-place operations. The system incorporates vacuum gripping technology with compliance control for optimal grasping and manipulation points, using a Franka Emika robot arm.ResultsThe system successfully demonstrates its capability to automate the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader. Rigorous lab testing showed high accuracy in bag detection and manipulation under varying environmental conditions, as well as reliable performance in handling and processing tasks. The approach effectively addressed challenges related to transparency, plastic bag manipulation and industrial automation.DiscussionThe results indicate that the proposed solution is highly effective for industrial applications requiring precision and adaptability, aligning with the principles of Industry 4.0. By combining advanced vision algorithms, depth sensing, and compliance control, the system offers a robust method for automating challenging tasks. The integration of cobots into such workflows demonstrates significant potential for enhancing efficiency, safety, and sustainability in industrial settings.

Research on Automatic Identification of Power Big Data Anomalies Based on Improved Decision Tree

With the advancement of smart grid technology, the issue of power system network security has become increasingly critical. To fully utilize the power grid’s vast data resources and enhance the efficiency of anomaly detection, this paper proposes an improved decision tree (DT)-based automatic identification approach for anomalies in electric power big data. The method employs six-dimensional features extracted from the dimensions of volatility, trend, and variability to characterize the time series of power data. These features are integrated into a hybrid DT-SVM-LSTM framework, combining the strengths of DTs, support vector machines, and long short-term memory networks. Experimental results demonstrate that the proposed method achieves an accuracy of 96.8%, a precision of 95.3%, a recall of 94.8%, and an F1-score of 95.0%, outperforming several state-of-the-art methods cited in the literature. Moreover, the approach exhibits strong robustness to noise, maintaining high detection accuracy even under low signal-to-noise ratio conditions. These findings highlight the effectiveness of the method in efficiently detecting anomalies and addressing noise interference.

YOLOv8s-Longan: a lightweight detection method for the longan fruit-picking UAV

IntroductionDue to the limited computing power and fast flight speed of the picking of unmanned aerial vehicles (UAVs), it is important to design a quick and accurate detecting algorithm to obtain the fruit position.MethodsThis paper proposes a lightweight deep learning algorithm, named YOLOv8s-Longan, to improve the detection accuracy and reduce the number of model parameters for fruitpicking UAVs. To make the network lightweight and improve its generalization performance, the Average and Max pooling attention (AMA) attention module is designed and integrated into the DenseAMA and C2f-Faster-AMA modules on the proposed backbone network. To improve the detection accuracy, a crossstage local network structure VOVGSCSPC module is designed, which can help the model better understand the information of the image through multiscale feature fusion and improve the perception and expression ability of the model. Meanwhile, the novel Inner-SIoU loss function is proposed as the loss function of the target bounding box.Results and discussionThe experimental results show that the proposed algorithm has good detection ability for densely distributed and mutually occluded longan string fruit under complex backgrounds with a mAP@0.5 of 84.3%. Compared with other YOLOv8 models, the improved model of mAP@0.5 improves by 3.9% and reduces the number of parameters by 20.3%. It satisfies the high accuracy and fast detection requirements for fruit detection in fruit-picking UAV scenarios.

GLUT1 as a generic biomarker enables near-infrared fluorescence molecular imaging guided precise intraoperative tumor detection in breast cancer.

Precise tumor excision is important but challenging in breast-conserving surgery (BCS). Tumor-specific fluorescence imaging may be used for intraoperative tumor detection and, therefore, to guide precise tumor excision. The aims of this study are to develop a glucose transporter 1 (GLUT1)-targeted near-infrared fluorescence tracer and evaluate its accuracy for breast cancer detection using fresh surgical breast specimens. Bioinformatic analysis was performed to compare GLUT1 expression between breast cancer and normal breast tissues. A GLUT1-targeted fluorescence imaging tracer WZB117-CY7.5 was developed. In combination with fluorescence imaging (FMI), its binding specificity to GLUT1 was examined in in vitro breast cancer cell experiments, in vivo 4T1 breast tumor-bearing mouse models, and 60 freshly resected human breast tumor tissues. The diagnostic accuracy of WZB117-CY7.5, was evaluated in fresh specimens derived from 60 patients diagnosed with breast cancer. GLUT1 expression is higher in breast cancer tissues compared with normal tissues. WZB117-CY7.5 specifically bound to breast cancer cells in in vitro cell experiments and accumulated in tumor areas in a 4T1 tumor-bearing mice after intravenous injection by FMI. Moreover, WZB117-CY7.5 specifically bound to freshly resected human breast cancer and demonstrated excellent diagnostic performance in discriminating breast cancer, irrespective of cancer subtype, from normal breast tissue on fresh surgically resected breast tissues. WZB117-CY7.5 showed high accuracy in intraoperative breast cancer detection, irrespective of the cancer subtype. This highlights its potential for clinical applications as a generic tracer for fluorescence image-guided surgery (FIGS) in BCS and fluorescence image-guided pathology for tissue sampling.

A hybrid human fall detection method based on modified YOLOv8s and AlphaPose

To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function. These methods can effectively improve the accuracy of small object detection and significantly reduce the model size. Secondly, we improve the fall recognition accuracy by introducing a hybrid fall detection algorithm based on human skeletal nodes. Lastly, we build a multi-person fall detection dataset (MPFDD) to test the model’s effectiveness in multi-person scenarios. Validated on datasets Le2i and MPFDD, our method improves accuracy by 4.30%, F1 by 4.57%, and FPS by 37.50% faster than the AlphaPose. Compared with other models, our model improves accuracy by 5.33% on average, F1 by 5.51%, and FPS by 43.05% faster on average. Therefore, HFDMIA-Pose has significantly improved performance compared to the original model and it also demonstrates strong competitiveness over other advanced human fall detection models. Furthermore, it has the advantages of high detection accuracy, fewer model size, and fast speed, which makes it more suitable for resource constrained edge environments and can meet industrial and daily scenarios.

Chip-scale integrated optical gyroscope based on a multi-mode co-detection technique

Gyroscopes are crucial components of inertial navigation systems, with ongoing development emphasizing miniaturization and enhanced accuracy. The recent advances in chip-scale optical gyroscopes utilizing integrated optics have attracted considerable attention, demonstrating significant advantages in achieving tactical-grade accuracy. In this paper, a new, to our knowledge, integrated optical gyroscope scheme based on the multi-mode co-detection technology is proposed, which takes the high-Q microcavity as its core sensitive element and uses the multi-mode characteristics of the microcavity to achieve the measurement of rotational angular velocity. This detection scheme breaks the tradition of optical gyroscopes based on a single mode within the sensitive ring to detect the angular rotation rate, which not only greatly simplifies the optical and electrical system of the optical gyroscope, but also has a higher detection accuracy. The gyroscope based on this detection scheme has successfully detected the Earth’s rotation on a 9.2 mm diameter microcavity with a bias instability as low as 1 deg/h, which is the best performance among the chip-scale integrated optical gyroscopes known to us. Moreover, its high dynamic range and highly simplified and reciprocal system architecture greatly enhance the feasibility of practical applications. It is anticipated that these developments will have a profound impact on the field of inertial navigation.

High-accuracy detection method for miner action recognition with shuffle attention SlowFast networks

High-accuracy detection method for miner action recognition with shuffle attention SlowFast networks

Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester

Aiming to address the problem of low recognition accuracy in the current cleaning loss detection system of rapeseed harvesters, a rapeseed cleaning loss detection system was developed using the energy distinction method based on the principle of impact piezoelectricity. A signal processing circuit, centered around a hardware integral circuit and a triple voltage comparison circuit, was designed. The energy of the impact signals generated by rapeseed kernels and impurities was calculated through hardware integration. The distinction threshold for the energy of the impact signals generated by kernels and impurities under the operating wind speed of the cleaning system was found through experiments, and a fitting model relating the fan speed to the distinction threshold was constructed. A loss detection and counting system for rapeseed kernels was designed to realize the statistics and real-time display of rapeseed kernels regarding cleaning loss. Performance verification tests were conducted on mixtures of rapeseed kernels and impurities with different mixing ratios, and a field test was carried out on the platform of a 4LZY-5.0Z rapeseed combine harvester. The test results showed that the accuracy of the designed loss detection system for kernel identification was more than 91.6%. Under operating conditions of 700, 900 and 1200 r/min fan speeds in the combine harvester cleaning system, the relative errors of the loss detection system compared to manual detection were 5%, 4.7% and 3.8%, respectively. The developed loss detection system for rapeseed kernels has high detection accuracy and good overall performance, which means it can provide feedback information for the control of the harvester.

Road Accident Emergency Reporting Management System using Neural Network

Given the high number of road accidents that result in deaths every day, victims may lose their lives if emergency medical personnel or concerned parties fail to arrive promptly to intervene in an accident or if they do so, because they are unaware of the location of a nearby hospital or medical facility, they may not be able to provide the much-needed help. To avert this scenario, this research work seeks to deploy a Deep Learning Road Accident Emergencies Management model, which by examining of video and sensors’ data, identifies and reports traffic incidents in real time. A Neural Network (NN) is used by the model to recognize accident scenarios based on anomalous motion patterns inside video frames, including sudden halts and crashes. Put into practice in python, the program incorporates deep learning libraries such as OpenCV and TensorFlow for strong data processing and analysis in real time. To train and evaluate the model, an 80/20 data split, which results in reduced latency and high accuracy in accident detection. The Key performance metrics include a testing accuracy of 96%, an F1-score of 87.5%, and a response time of less than two seconds on average. The study advances the domains of artificial intelligence, transportation safety, and emergency management by offering a practical, real-time solution for automated accident reporting. This quick detection capability allows for timely emergency alerts, which are sent to responders via a web-based interface, giving them precise location details. The model's promise as a life-saving tool in smart city infrastructures is highlighted by its capacity to deliver precise and prompt replies. The Road Accident Emergency Reporting Model has important ramifications for increasing road safety, decreasing emergency response times, and boosting public safety in general due to its strong design and low latency.

Characterization of adrenal glands on computed tomography with a 3D V-Net-based model

ObjectivesTo evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.MethodsA total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes.ResultsThe DSC of the test set of the segmentation model was 0.900 (0.810–0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively).ConclusionThe 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands.Critical relevance statementA 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene.Key PointsAdrenal lesions may be prone to inter-observer variability in routine diagnostic workflow.The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set.The model showed high sensitivity and accuracy of abnormalities detection in different scenes.Graphical

Underwater Target Detection with High Accuracy and Speed Based on YOLOv10

Underwater target detection exhibits extensive applications in marine target exploration and marine environmental monitoring. However, conventional images of underwater targets present challenges including blurred contour information, complex environmental conditions, and pronounced scattering effects. In this work, an underwater target detection method based on YOLOv10 is designed, and the detection performance is compared with the YOLOv5 model. Experimental results demonstrate that the YOLOv10 model has a mAP50 of 85.6% on the URPC 2020 dataset, improving the mAP50 by 1.2% than that of YOLOv5. This model exhibits high detection accuracy and high proceeding speed, which provides a promising support for precise and fast underwater target detection.

Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices.

The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (APS: 14.3) and large tumor localization (APL: 49.7). Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. Clinical relevance is a key focus of this work. The proposed model addresses the diagnostic challenges of small, variable-sized tumors often overlooked by existing methods. Its lightweight architecture enables accurate and timely tumor localization on portable devices, bridging the gap in diagnostic accessibility for underserved regions. Extensive experiments on the BRATS dataset demonstrate the model robustness across tumor sizes and configurations, with confidence scores consistently exceeding 81%. This advancement holds the potential for improving early tumor detection, particularly in remote areas lacking advanced medical infrastructure, thereby contributing to better patient outcomes and broader accessibility to AI-driven diagnostic tools.

YOLOv7-CWFD for real time detection of bolt defects on transmission lines

Detecting bolt defects on transmission lines is crucial for ensuring the safe operation of the electrical power system. However, existing methods for detecting bolt defects on transmission lines require higher detection accuracy and smaller model sizes. To address these challenges, this paper proposes a real-time bolt defect detection model based on YOLOv7, named YOLOv7-CWFD. The model integrates the Channel Shuffle Diverse Path Aggregation Network (CSDPAN), significantly reducing computational and parameter complexity while maintaining high detection accuracy. Additionally, weighted Efficient Intersection over Union (EIoU) and Normalized Wasserstein Distance (NWD) loss functions are designed to reduce the network’s sensitivity to object size variations and enhance model convergence in regression tasks. The Fast Fourier Channel Attention Mechanism (FFCAM) is introduced between the backbone and neck fusion networks to mitigate excessive smoothing of detailed information and improve the network’s sensitivity to objects. The DySample upsampling operator is implemented to replace the upsampling module in the neck fusion network, minimizing information loss during the upsampling process. Experiments conducted on the custom Transmission Line Bolt Defect Dataset (TLBDD) demonstrate a reduction of 10.30MB in model parameter size, along with a 2.30% increase in mean Average Precision (mAP) compared with the original YOLOV7 and a detection speed of 51.15 frames per second (FPS). Experiments on the public dataset CCTSDB further confirm the model’s robust generalization capability. These experiments validate the effectiveness of the proposed algorithm.

XGBoost Enhances the Performance of SAFE: A Novel Microwave Imaging System for Early Detection of Malignant Breast Cancer.

Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks. This highlights the need for alternative screening methods. In this study, we assess the performance of SAFE (Scan and Find Early), a novel microwave imaging device, in detecting breast cancer in a larger patient cohort. Unlike previous studies that predominantly relied on cross-validation, this study employs a more reliable, independent evaluation methodology to enhance generalizability. We developed an XGBoost model to classify breast cancer cases into positive (malignant) and negative (benign or healthy) groups. The model was analyzed with respect to key factors such as breast size, density, age, tumor size, and histopathological findings. This approach provides a better understanding of how these factors influence the model's performance, using an independent evaluation methodology for increased reliability. Our results demonstrate that SAFE exhibits high sensitivity, particularly in dense breasts (91%) and younger patients (83%), suggesting its potential as a supplemental screening tool. Additionally, the system shows high detection accuracy for both small (<2 cm) and larger lesions, proving effective in early cancer detection. This study reinforces the potential of SAFE to complement existing screening methods, particularly for patients with dense breasts, where mammography's sensitivity is reduced. The promising results warrant further research to solidify SAFE's clinical application as an alternative screening tool for breast cancer detection.

A Novel Ensemble Approach for Rib Fracture Detection and Visualization using CNNs and Grad-CAM.

This study aimed to develop a reliable and efficient system for predicting and locating rib fractures in medical images using an ensemble of convolutional neural networks (CNNs). We employed five CNN architectures-Visual Geometry Group Network 16 (VGG16), Densely Connected Convolutional Network 169 (DenseNet169), Inception Version 4 (Inception V4), Efficient Network B7 (EfficientNet-B7), and Residual Network Next 50 layers (ResNeXt-50)-trained on a dataset of 840 grayscale computed tomography (CT) scan images in .jpg format collected from 42 patients at a local hospital. The images were categorized into two groups representing healed and fresh fractures. The ensemble model was designed to improve predictive accuracy and robustness, utilizing techniques like gradient-weighted class activation mapping (Grad-CAM) for visualization of fracture locations. The ensemble model achieved an accuracy of 0.96, area under the curve (AUC) of 0.97, recall of 0.97, and F1 score of 0.96. Grad-CAM visualizations could effectively locate rib fractures, providing crucial assistance in diagnostics. The ensemble model demonstrates high accuracy and robustness in fracture detection, underscoring its potential for enhancing diagnostic processes in clinical settings. Despite limitations such as the small dataset size and lack of diverse demographic representation, the results are promising for future clinical application.

Fault Arc Detection Method Based on Improved ShuffleNet V2 Network

Fault arcs exhibit randomness, with current waveforms closely mirroring those of standard nonlinear load operations, posing challenges for traditional series fault arc detection methods. This study presents an improved detection approach using a lightweight convolutional neural network model, ShuffleNet V2. Current data from household loads were collected and preprocessed to establish a comprehensive database, leveraging one-dimensional convolution and channel attention mechanisms for precise analysis. Experimental results demonstrate a high fault arc detection accuracy of 97.8%, supporting real-time detection on the Jetson Nano embedded platform, with an efficient detection cycle time of 15.65 ms per sample. The proposed approach outperforms existing methods in both accuracy and speed, providing a robust foundation for developing advanced fault arc circuit breakers.

Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage.

The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage. This is an important advantage that offers the opportunity to identify damage with sensors whose position does not have to coincide with the damaged area. The integration of MEMS sensors into structural health monitoring (SHM) systems offers a promising approach to long-term structural maintenance, especially in large-scale infrastructure. This paper presents an anomaly detection technique that analyzes raw sequential data within a statistical framework to detect damage that causes prestress loss of the tendon by exploiting a distributed monitoring system composed of six high-performance MEMS sensors. The proposed system is preliminarily evaluated to identify the frequency of the first mode, and then the proposed methodology is validated on acceleration data collected on a 240 cm beam in three different damage configurations, achieving a high detection accuracy and showing that its output can also evaluate the damage localization.

Lightweight Lung-nodule Detection Model Combined with Multidimensional Attention Convolution.

Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications. The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results. In this study, based on YOLOv5s, a concentrated-comprehensive convolution (C3_ODC) module with multidimensional attention is designed in the convolutional layer of the original backbone network for enhancing the feature-extraction capabilities of the model. Moreover, lightweight convolution is combined with weighted bidirectional feature pyramid networks (BiFPNs) to form a GS-BiFPN structure that enhances the fusion of multiscale features while reducing the number of model parameters. Finally, Focal Loss is combined with the normalized Wasserstein distance (NWD) to optimize the loss function. Focal loss focuses on carcinoma-positive samples to mitigate class imbalance, whereas the NWD enhances the detection performance of small lung nodules. In comparison experiments against the YOLOv5s, the proposed model improved the average precision by 8.7% and reduced the number of parameters and floating-point operations by 5.4% and 8.2%, respectively, while achieving 116.7 frames per second. The proposed model balances high detection accuracy against real-time requirements.