Improve Detection Performance Research Articles

Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation

Advancements in automation and artificial intelligence have significantly impacted accessibility for individuals with visual impairments, particularly in the realm of bus public transportation. Effective bus detection and bus point-of-view (POV) classification are crucial for enhancing the independence of visually impaired individuals. This study introduces the Improved-YOLOv10, a novel model designed to tackle challenges in bus identification and pov classification by integrating Coordinate Attention (CA) and Adaptive Kernel Convolution (AKConv) into the YOLOv10 framework. The Improved YOLOv10 advances the YOLOv10 architecture through the incorporation of CA, which enhances long-range dependency modeling and spatial awareness, and AKConv, which dynamically adjusts convolutional kernels for superior feature extraction. These enhancements aim to improve both detection accuracy and efficiency, essential for real-time applications in assistive technologies. Evaluation results demonstrate that the Improved-YOLOv10 offers significant improvements in detection performance, including better Accuracy, Precision and Recall compared to YOLOv10. The model also exhibits reduced computational complexity and storage requirements, highlighting its efficiency. While the classification results show some trade-offs, with slightly decreased overall F1 score, the complexity of Giga Floating Point Operations (GFLOPs), Parameters, and Weight/MB in the Improved-YOLOv10 remains advantageous for classification tasks. The model’s architectural improvements contribute to its robustness and efficiency, making it a suitable choice for real-time applications and assistive technologies.

DCFE-YOLO: A novel fabric defect detection method.

Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture details. Second, a Channel Priority Convolutional Attention mechanism is incorporated after the Spatial Pyramid Pooling layer to enable more precise defect localization by leveraging multi-scale structures and channel priors. Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. Experimental results demonstrate a significant improvement in detection performance. Specifically, mAP@0.5 increased by 2.9%, precision improved by 3.5%, and mAP@0.5:0.95 rose by 2.3%, highlighting the model's superior capability in detecting complex defects. The project is available at https://github.com/lilian998/fabric.

Comparison of Dimensionality Reduction Techniques to Improve Performance and Efficiency of Logistic Regression in Network Anomaly Detection

Network anomaly detection is a crucial process to identify abnormal network traffic, which may pose a security threat. This research aims to improve the performance and efficiency of Logistic Regression (LR) in network anomaly detection by applying dimension reduction techniques, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (TSVD), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). The performance of each dimension reduction method is evaluated based on accuracy, precision, recall, F1-score, and computation time. The results show that TSVD provides the best performance with 95.86% accuracy, 0.96 precision, 0.96 recall, 0.95 F1-score, and 13.83 seconds computation time. In contrast, ICA showed the worst performance, especially in precision, recall, and F1-score, with values of 0.73, 0.83, and 0.78, respectively. Meanwhile, although t-SNE produces competitive accuracy, it has a high computational cost with an execution time of 1698.54 seconds. These findings show that choosing the right dimension reduction algorithm not only improves detection performance but also supports data processing efficiency, making it highly relevant for large-scale network security scenarios. Keywords: dimensionality reduction, Logistic Regression, network anamoly detection, performance evaluation, Truncated Singular Value Decomposition.

Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification

Objective: This study evaluated the applicability of the classical method, height loss ratio (HLR), for identifying major acute compression fractures in clinical practice and compared its performance with deep learning (DL)-based VCF detection methods. Additionally, it examined whether combining the HLR with DL approaches could enhance performance, exploring the potential integration of classical and DL methodologies. Methods: End-to-End VCF Detection (EEVD), Two-Stage VCF Detection with Segmentation and Detection (TSVD_SD), and Two-Stage VCF Detection with Detection and Classification (TSVD_DC). The models were evaluated on a dataset of 589 patients, focusing on sensitivity, specificity, accuracy, and precision. Results: TSVD_SD outperformed all other methods, achieving the highest sensitivity (84.46%) and accuracy (95.05%), making it particularly effective for identifying true positives. The complementary use of DL methods with HLR further improved detection performance. For instance, combining HLR-negative cases with TSVD_SD increased sensitivity to 87.84%, reducing missed fractures, while combining HLR-positive cases with EEVD achieved the highest specificity (99.77%), minimizing false positives. Conclusion: These findings demonstrated that DL-based approaches, particularly TSVD_SD, provided robust alternatives or complements to traditional methods, significantly enhancing diagnostic accuracy for acute VCFs in clinical practice.

Impurity detection of premium green tea based on improved lightweight deep learning model.

Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in this industry, this article uses deep learning technology to detect impurities in premium green tea. However, in actual production, computing resources are limited by equipment, and large models are not suitable for deployment. On this basis, a lightweight model for detecting impurities in premium green tea is proposed. We conducted experiments on You Only Look Once version 8 (YOLOv8) models on a self-made dataset and selected the basic model and the teacher model. By replacing the loss function and lightweight convolution, model pruning, and knowledge distillation, the model achieves lightweighting while improving detection performance. The experimental results reveal that the Giga floating point operations (GFLOPs), parameters, precision (P), recall (R), mean average precision (mAP), and frames per second (FPS) of the improved model are 4.2, 791966 B, 0.9379, 0.8959, 0.9484, and 1362.7, respectively. Compared with the original model, the P, R, mAP, and FPS are improved by 0.0216, 0.0320, 0.0261, and 368.0, respectively. The GFLOPs and parameters are reduced by 3.9 (48.15%) and 2214462 B (73.66%), respectively. This study provides technical support for the intelligent sorting of impurities in premium green tea.

Underwater moving target detection using online robust principal component analysis and multimodal anomaly detection.

In shallow water, reverberation complicates the detection of low-intensity, variable-echo moving targets, such as divers. Traditional methods often fail to distinguish these targets from reverberation, and data-driven methods are constrained by the limited data on intruding targets. This paper introduces the online robust principal component analysis and multimodal anomaly detection (ORMAD) method to address these challenges. ORMAD efficiently performs online low-rank and sparse decomposition while utilizing unsupervised multimodal anomaly detection to enhance detection performance. The multimodal anomaly detection process involves two phases: modality extraction and anomaly detection. During modality extraction, echo data are separated into echo structure and spatial trajectory modalities, providing complementary information that improves the network representation of both reverberation and moving targets. The subsequent anomaly detection phase unsupervisedly learns the modalities of fluctuating reverberation, thereby achieving stable reconstruction while maintaining sensitivity to moving targets. This sensitivity allows effective identification of moving targets by detecting reconstruction loss. Experimental results demonstrate that ORMAD effectively improves detection performance in complex reverberation scenarios. In a real-world sonar dataset, ORMAD increased the average precision for detecting diver targets from 60% to 75% compared to the state-of-the-art method.

Study on Improving Detection Performance of Wildfire and Non-Fire Events Early Using Swin Transformer

Study on Improving Detection Performance of Wildfire and Non-Fire Events Early Using Swin Transformer

Waste drilling fluid flocculation identification method based on improved YOLOv8n.

Efficient identification of the flocculation state of waste drilling fluid remains a significant challenge. This study proposes an improved You Only Look Once version 8 nano-algorithm (YOLOv8n), specifically optimized for real-time monitoring of drilling fluid flocculation under field conditions. The algorithm employs MobileNetV3 as the backbone network to minimize memory usage, improve detection speed, and reduce computational requirements. The integration of the efficient multi-scale attention mechanism into the cross-stage partial fusion module effectively mitigates detail loss, resulting in improved detection performance for images with high similarity. The wise intersection over union loss function is employed to accelerate bounding box convergence and improve inference accuracy. Experimental results show that the enhanced YOLOv8n algorithm achieves an average recognition accuracy of 98.6% on the experimental dataset, a 4.8% improvement over the original model. In addition, the model size and parameter count are reduced to 2.9 MB and 2.8 Giga Floating-Point Operations Per Second (GFLOPS), respectively, compared to the original model, reflecting a reduction of 3.2 MB and 5.3 GFLOPS. As a result, the proposed flocculation recognition algorithm is highly deployable and effectively predicts flocculation state changes across varying working conditions.

Vacuum degree detection performance improvement of vacuum switches based on NELIBS

We aimed to investigate the enhancement effects of silver nanoparticles on signals under low-pressure. By analyzing spectral data, plasma images, and radiation integral intensity, we seek to improve the accuracy of vacuum level measurements.

BGI-YOLO: Background Image-Assisted Object Detection for Stationary Cameras

This paper proposes a method enhancing the accuracy of object detectors by utilizing background images for stationary camera systems. Object detection with stationary cameras is highly valuable across various applications, such as traffic control, crime prevention, and abnormal behavior detection. Deep learning-based object detectors, which are mainly used in such cases, are developed for general purposes and do not take advantage of stationary cameras at all. Previously, cascade-based object detection methods utilizing background have been studied for stationary camera systems. These methods typically consist of two stages: background subtraction followed by object classification. However, their object detection performance is highly dependent on the accuracy of the background subtraction results, and numerous parameters must be adjusted during background subtraction to adapt to varying conditions. This paper proposes an end-to-end object detection method named BGI-YOLO, which uses a background image simply by combining it with an input image before feeding it into the object detection network. In our experiments, the following five methods are compared: three candidate methods of combining input and background images, baseline YOLOv7, and a traditional cascade method. BGI-YOLO, which combines input and background images at image level, showed a detection performance (mAP) improvement compared to baseline YOLOv7, with an increase of 5.6%p on the WITHROBOT S1 dataset and 2.5%p on the LLVIP dataset. In terms of computational cost (GFLOPs), the proposed method showed a slight increase of 0.19% compared to baseline YOLOv7. The experimental results demonstrated that the proposed method is highly effective for improving detection accuracy without increasing computational cost.

Enhanced Feature Extraction with AL-YOLOv9s Lightweight Model: Application in Key Component Recognition Within Highly Integrated Device Environments

In environments containing highly integrated devices, accurately monitoring the status of circuit breaker lockouts is essential for maintaining the stability of power systems. Traditional detection methods are often inadequate due to complex equipment configurations and severe operational challenges. This paper presents an enhanced detection model, the AL-YOLOv9s, which improves the efficiency and accuracy of detecting circuit breaker lockouts. The AL-YOLOv9s model is based on the advanced YOLOv9s algorithm and incorporates an enhanced efficient multi-scale attention module to boost feature extraction capabilities. It also integrates channel and spatial attention mechanisms to optimize the feature fusion process, thereby improving detection performance. Additionally, the model has been optimized to a size of 4.7M, making it suitable for lightweight field applications without compromising accuracy. Experimental results demonstrate that the AL-YOLOv9s model achieves high standards in accuracy and portability, thus offering an effective and practical solution for lockout detection.

Enhanced oracle bone corrosion detection using attention-guided YOLO with ghost convolution

Oracle bone inscriptions (OBIs), as the important records of early Chinese characters, possess profound cultural and historical significance. However, These fragments are susceptible to further damage due to corrosion. Consequently, the identification and localization of corroded regions on these fragments to prevent further deterioration has emerged as a critical task. Current object detection algorithms exhibit limitations in identifying corroded regions on oracle bone fragments, which is manifested by suboptimal detection accuracy, F1 scores, and average precision (AP) values. This deficiency hinders their capability to effectively manage the complexity and diversity of corrosion patterns present on oracle bone fragments. To tackle this challenge, this study incorporates advanced attention mechanisms, including Squeeze-and-Excitation Networks (SE), Coordinate attention, and Convolutional Block Attention Module (CBAM), into the YOLOv5 detection architecture. Additionally, we introduce Ghost convolution into the backbone network to effectively retain critical feature map information while optimizing computational efficiency. Our experimental results indicate that the integration of CBAM attention and Ghost convolution within the YOLOv5 backbone markedly improves the detection accuracy of corroded regions on oracle bone fragments. Specifically, compared to the baseline YOLOv5 model, the proposed models incorporating SE, CBAM, and Ghost convolution obtain the improvements of 2.2%, 6.3%, and 8.5% in AP, respectively. This improvement in detection performance not only facilitates the identification of corroded areas but also contributes to the preservation of oracle bone heritage and the continuity of cultural knowledge.

High-Precision Traffic Sign Detection and Recognition Using an Enhanced YOLOv5

High-precision traffic sign detection plays an important role in enhancing road traffic safety, ensuring traffic smoothness, supporting the development of intelligent transport systems and promoting the standardization and normalization of traffic facilities. To overcome the limitations of traditional methods, this study proposes an enhanced YOLOv5 algorithm for complex road environments. First, the K-Means clustering algorithm is used to cluster and analyze the boundary boxes of traffic signs in the training dataset, obtaining anchor box sizes that are closer to the distribution of the dataset to improve detection accuracy. Then, a genetic algorithm was used to further optimize the initial anchor box, and a global search strategy was used to find the optimal anchor box configuration, further improving detection performance. In terms of model structure, a bidirectional feature pyramid network (Bi-FPN) is introduced, which effectively utilizes multi-scale feature information and enhances the adaptability of the model to traffic signs of different sizes through top-down and bottom-up feature fusion paths, as well as cross-scale connections. In addition, a global attention mechanism (GAM) was introduced to recalibrate the feature maps through channel attention and spatial attention dimensions, improving the robustness and detection accuracy of the model for complex environments. Finally, a loss function called Focal-EIoU was used to solve the problems of class imbalance and sample imbalance, which improved the stability and performance of the object detection model. Experiments on a Chinese traffic sign dataset demonstrate significant improvements, with an increase in mAP by 10.03%, precision by 4.7%, recall by 2.6% and F1-score by 3.48%, respectively. The results prove the validity of the proposed traffic sign recognition method, especially in complex road environments. This study provides new ideas and methods for the field of traffic sign detection, which has important theoretical significance and application value.

YH-RTYO: an end-to-end object detection method for crop growth anomaly detection in UAV scenarios

Background Small object detection via unmanned Aerial vehicle (UAV) is crucial for smart agriculture, enhancing yield and efficiency. Methods This study addresses the issue of missed detections in crowded environments by developing an efficient algorithm tailored for precise, real-time small object detection. The proposed Yield Health Robust Transformer-YOLO (YH-RTYO) model incorporates several key innovations to advance conventional convolutional models. The model features an efficient convolutional expansion module that captures additional feature information through extended branches while maintaining parameter efficiency by consolidating features into a single convolution during validation. It also includes a local feature pyramid module designed to suppress background interference during feature interaction. Furthermore, the loss function is optimized to accommodate various object scales in different scenes by adjusting the regression box size and incorporating angle factors. These enhancements collectively contribute to improved detection performance and address the limitations of traditional methods. Result Compared to YOLOv8-L, the YH-RTYO model achieves superior performance in all key accuracy metrics, with a 13% reduction in the scale of model. Experimental results demonstrate that the YH-RTYO model outperforms others in key detection metrics. The model reduces the number of parameters by 13%, facilitating deployment while maintaining accuracy. On the OilPalmUAV dataset, it achieves a 3.97% improvement in average precision (AP). Additionally, the model shows strong generalization on the RFRB dataset, with AP50 and AP values exceeding those of the YOLOv8 baseline by 3.8% and 2.7%, respectively.

Enhanced Intrusion Detection for ICS Using MS1DCNN and Transformer to Tackle Data Imbalance.

With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network security while addressing challenges related to data imbalance and long-tail distributions. A dataset containing five types of attacks targeting programmable logic controllers (PLCs) in industrial control systems (ICS) was first constructed. To address class imbalance and challenges posed by complex network traffic, Synthetic Minority Oversampling Technique (SMOTE) and Borderline-SMOTE were applied to oversample minority classes, thereby enhancing their diversity. This paper proposes a dual-channel feature extraction model that integrates a multi-scale one-dimensional convolutional neural network (MS1DCNN) and a Weight-Dropped Transformer (WDTransformer) for IDS. The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail distributions, a custom combined loss function was developed by integrating cross-entropy loss and focal loss to reduce misclassification in minority classes. Experimental validation on the constructed dataset demonstrated that the proposed model achieved an accuracy of 95.11% and an F1 score of 95.12%, significantly outperforming traditional machine learning and deep learning models.

A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection.

Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces a weather-adaptive Convolutional Neural Network (CNN) framework that leverages the YOLOv10 model that is designed to enhance license plate detection in adverse weather conditions. By incorporating weather-specific data augmentation techniques, our framework improves the robustness of ALPR systems under diverse environmental scenarios. We evaluate the effectiveness of this approach using metrics such as precision, recall, F1, mAP50, and mAP50-95 score across various model configurations and augmentation strategies. The results demonstrate a significant improvement in overall detection performance, particularly in challenging weather conditions. This study provides a promising solution for deploying resilient ALPR systems in regions with similar environmental complexities.

Camouflaged target detection based on infrared saliency map and visible image fusion

Abstract In order to effectively improve the detection ability of camouflaged targets and fully utilize the potential of target detection algorithms, this paper proposes a camouflaged target detection method based on the fusion of thermal infrared saliency map and visible image. Firstly, the saliency map of the target’s infrared band is obtained through the instance segmentation network dedicated to the camouflaged target, and then the saliency map is fused with the target’s visible image with pixel-level weighting, thus obtaining the fused image of the same target in two bands, and this fusion method is aimed at the enhancement of specific regions of the camouflaged target. The target detection by YOLOv8, the experimental results show that the method of this paper has a significant improvement in image detection performance, can effectively improve the recognition ability of camouflage targets.

ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness.

Recently, graph anomaly detection on attributed networks has attracted growing attention in data mining and machine learning communities. Apart from attribute anomalies, graph anomaly detection also aims at suspicious topological-abnormal nodes that exhibit collective anomalous behavior. Closely connected uncorrelated node groups form uncommonly dense substructures in the network. However, existing methods overlook that the topology anomaly detection performance can be improved by recognizing such a collective pattern. To this end, we propose a new graph anomaly detection framework on attributed networks via substructure awareness (ARISE). Unlike previous algorithms, we focus on the substructures in the graph to discern abnormalities. Specifically, we establish a region proposal module to discover high-density substructures in the network as suspicious regions. The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures. Generally, the lower the similarity, the higher the probability that internal nodes are topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, ARISE can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that ARISE greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection (ANAD) algorithms.

Application of classification algorithms for smishing detection on mobile devices: literature review

Smishing is a form of phishing carried out via mobile devices to steal confidential information from victims. The number of smishing attacks has increased in recent years due to the large number of users acquiring these easy-to-use and functional devices. This literature review objective is to examine the techniques and methods used in smishing attacks using classification algorithms. To do so, we conducted a manual search process and selected 155 articles from Scopus and 29 articles from access to research for development and innovation (ARDI). Of these, 36 articles met the inclusion criteria. In addition, the algorithms most commonly used by the studies were random forest classification techniques, decision trees, and neural networks. These studies analyzed various machine learning models for detecting phishing and smishing messages. The attack simulation scenarios included generating web pages, sending fake links (URLs), and installing malicious applications. The analysis evaluated web pages and SMS messages using a database containing legitimate as well as smishing messages. Based on the results, it is suggested to combine these methods to improve detection performance, making it more robust and promising.

Fast Quality Detection of Astragalus Slices Using FA-SD-YOLO

Quality inspection is a pivotal component in the intelligent sorting of Astragalus membranaceus (Huangqi), a medicinal plant of significant pharmacological importance. To improve the precision and efficiency of assessing the quality of Astragalus slices, we present the FA-SD-YOLO model, an innovative advancement over the YOLOv8n architecture. This model introduces several novel modifications to enhance feature extraction and fusion while reducing computational complexity. The FA-SD-YOLO model replaces the conventional C2f module with the C2F-F module, developed using the FasterNet architecture, and substitutes the SPPF module with the Adaptive Inverted Fusion (AIFI) module. These changes markedly enhance the model’s feature fusion capabilities. Additionally, the integration of the SD module into the detection head optimizes parameter efficiency while improving detection performance. Performance evaluation highlights the superiority of the FA-SD-YOLO model. It achieves accuracy and recall rates of 88.6% and 89.6%, outperforming the YOLOv8n model by 1.8% and 1.3%, respectively. The model’s F1 score reaches 89.1%, and the mean average precision (mAP) improves to 93.2%, reflecting increases of 1.6% and 2.4% over YOLOv8n. These enhancements are accompanied by significant reductions in model size and computational cost: the parameter count is reduced to 1.58 million (a 47.3% reduction), and the FLOPS drops to 4.6 G (a 43.2% reduction). When compared with other state-of-the-art models, including YOLOv5s, YOLOv6s, YOLOv9t, and YOLOv11n, the FA-SD-YOLO model demonstrates superior performance across key metrics such as accuracy, F1 score, mAP, and FLOPS. Notably, it achieves a remarkable recognition speed of 13.8 ms per image, underscoring its efficiency and suitability for real-time applications. The FA-SD-YOLO model represents a robust and effective solution for the quality inspection of Astragalus membranaceus slices, providing reliable technical support for intelligent sorting machinery in the processing of this important medicinal herb.