Detection Algorithm Research Articles

A fault detection and location algorithm for the LVDC interconnection network in rural area

AbstractLow voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low‐income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies.

A lightweight defect detection algorithm for escalator steps

In this paper, we propose an efficient target detection algorithm, ASF-Sim-YOLO, to address issues encountered in escalator step defect detection, such as an excessive number of parameters in the detection network model, poor adaptability, and difficulties in real-time processing of video streams. Firstly, to address the characteristics of escalator step defects, we designed the ASF-Sim-P2 structure to improve the detection accuracy of small targets, such as step defects. Additionally, we incorporated the SimAM (Similarity-based Attention Mechanism) by combining SimAM with SPPF (Spatial Pyramid Pooling-Fast) to enhance the model’s ability to capture key information by assigning importance weights to each pixel. Furthermore, to address the challenge posed by the small size of step defects, we replaced the traditional CIoU (Complete-Intersection-over-Union) loss function with NWD (Normalized Wasserstein Distance), which alleviated the problem of defect missing. Finally, to meet the deployment requirements of mobile devices, we performed channel pruning on the model. The experimental results showed that the improved ASF-Sim-YOLO model achieved an average accuracy (mAP50) of 96.8% on the test data set, which was a 22.1% improvement in accuracy compared to the baseline model. Meanwhile, the computational complexity (in GFLOPS) of the model was reduced to a quarter of that of the baseline model, while the frame rate (FPS) was improved to 575.1. Compared with YOLOv3-tiny, YOLOv5s, YOLOv8s, Faster-RCNN, TOOD, RTMDET and other deep learning-based target recognition algorithms, ASF-Sim-YOLO has better detection accuracy and real-time processing capability. These results demonstrate that ASF-Sim-YOLO effectively balances lightweight design and performance improvement, making it highly suitable for real-time detection of step defects, which can meet the demands of escalator inspection operations.

UAV Hunter: A Net-Capturing UAV System with Improved Detection and Tracking Methods for Anti-UAV Defense

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs.

A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection

In the manufacturing process of printed circuit boards (PCBs), surface defects have a significant negative impact on product quality. Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized defects, this paper proposes a PCB defect detection algorithm based on a novel YOLOv5 multi-scale attention mechanism(EMA) spatial pyramid dilated Convolution (SPD-Conv) (YOLOv5_ES) network improved YOLOv5s framework. Firstly, the detection head is optimized by removing medium and large detection layers, fully leveraging the capability of the small detection head to identify minor target defects. This approach not only improves model accuracy but also achieves lightweighting. Secondly, in order to further reduce the number of parameters and computational costs, the SPD-Conv is introduced to improve the feature extraction capability by reducing information loss. Thirdly, a EMA module is introduced to fuse context information of different scales, enhancing the model’s generalization ability. Compared to the YOLOv5s model, there is a 3.1% improvement in mean average precision (mAP0.5), a 55.8% reduction in model parameters, and a 4.8% reduction in giga floating-point operations per second (GFLOPs). These results demonstrate a significant improvement in both accuracy and model parameter efficiency.

Comparative analysis of deep learning algorithms for seizure detection

Comparative analysis of deep learning algorithms for seizure detection

Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops

This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.

Complex Scene Understanding and Object Detection Algorithm Assisted by Artificial Intelligence

The purpose of this paper is to study the algorithm of object detection and scene understanding in complex scenes assisted by AI(Artificial Intelligence), so as to improve the machine's perception of the surrounding environment. Aiming at occlusion and dense scenes, this paper proposes a series of innovative algorithms by introducing technologies such as multi-scale detection, occlusion processing and real-time optimization. Experimental results show that the proposed algorithm has achieved excellent performance on several public data sets. Compared with the current mainstream object detection algorithm -YOLO, the method proposed in this paper has a significant improvement in accuracy. Especially in multi-scale object detection, occlusion and dense scene processing and real-time optimization, the proposed algorithm shows obvious advantages. The research results provide an effective solution for object detection and scene understanding in complex scenes, and promote the application of AI system in related fields.

An Efficient UAV Image Object Detection Algorithm Based on Global Attention and Multi-Scale Feature Fusion

An Efficient UAV Image Object Detection Algorithm Based on Global Attention and Multi-Scale Feature Fusion

A Safety Helmet Target Detection Algorithm Based on Improved YOLOv7

A Safety Helmet Target Detection Algorithm Based on Improved YOLOv7

Grayscale Iterative Star Spot Extraction Algorithm Based on Image Entropy

Star trackers are susceptible to interference from stray light, such as sunlight, moonlight, and Earth atmosphere light, in the space environment, resulting in an overall improvement in the star image grayscale, poor background uniformity, low star extraction rate, and high number of false star spots. In response to these challenges, this paper proposes a grayscale iterative star spot extraction algorithm based on image entropy. The implementation of the algorithm is mainly divided into two steps: (1) The algorithm conducts multiple grayscale iterations, effectively utilizing the prior information on the local contrast of star spots to filter out stray light backgrounds to a certain extent. (2) By establishing an inner–outer template, the image entropy algorithm is employed to obtain the real star targets to be extracted, which further suppresses the background clutter and noise. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this algorithm can effectively suppress background stray light, enhance star extraction rates, and reduce the number of false star spots, and it exhibits superior detection performance in complex backgrounds across various scenarios.

Contour Detection Algorithm for α Phase Structure of TB6 Titanium Alloy fused with Multi-Scale Fretting Features

Aiming at the problems of inaccuracy in detecting the α phase contour of TB6 titanium alloy. By combining computer vision technology with human vision mechanisms, the spatial characteristics of the α phase can be simulated to obtain the contour accurately. Therefore, an algorithm for α phase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed. Firstly, through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting, the information maps of the α phase contour of the TB6 titanium alloy at different scales are obtained; then the information map of the smallest scale contour is used as a benchmark, the neighborhood is constructed to judge the deviation of other scale contour information, and the corresponding weight value is calculated; finally, Gaussian function is used to weight and fuse the deviation information, and the contour detection result of TB6 titanium alloy α phase is obtained. In the Visual Studio 2013 environment, 484 metallographic images with different temperatures, strain rates, and magnifications were tested. The results show that the performance evaluation F value of the proposed algorithm is 0.915, which can effectively improve the accuracy of α phase contour detection of TB6 titanium alloy.

Detecting neoclassical tearing modes in high-temperature ITER plasma scenarios with the ITER prototype electron cyclotron emission diagnostic

Abstract Electron cyclotron emission (ECE) diagnostics for ITER serve two key purposes. The diagnostics will measure plasma electron temperature with high spatial and temporal resolution. Additionally, they will be used to detect neoclassical tearing modes (NTMs), a deleterious and nonlinearly unstable mode causing the growth of magnetic ‘seed’ islands. Interpreting ECE requires anticipation of physical limits including frequency cut-offs and harmonic overlap. In high temperature plasmas, the relativistic shift and broadening of the emission must also be considered to accurately reconstruct the electron temperature spatial profile. Accounting for these effects allows ECE diagnostics to be used for accurate measurement of the equilibrium electron temperature profile, as well as fluctuations about this equilibrium. One such fluctuation is caused by the fast radial transport of heat across rotating magnetic islands. ECE diagnostics can detect this change as an oscillation at the plasma rotation frequency to determine the existence and location of NTMs. This paper presents work on a synthetic diagnostic for ECE. The synthetic diagnostic tests simulated ECE signals, which are inferred from ITER scenarios perturbed by magnetic islands after accounting for all ECE physics. The synthetic diagnostic tests conventional ECE detection algorithms for NTMs in real-time on ITER-recommended hardware. Combined, these two areas of focus help determine design of the ECE system.

The Impact of Machine Learning-based Student Behaviour Detection on Teaching in Teaching Classroom Scenarios

The purpose of this study is to address the issue of low efficiency and time-consuming monitoring of student classroom behaviour in teaching classroom scenarios, as well as how to apply deep learning to student classroom behaviour detection. Classroom teaching is the main way for students to acquire and master knowledge, and teachers need to pay attention to students’ behaviour in the classroom and make timely corrections. In this study, the teaching classroom of undergraduate students was used as an example for application and analysis. Based on the You Only Look Once v5s network, a student behaviour detection algorithm was designed by combining the ghosting module and optimised feature fusion structure, and a student behaviour association technology suitable for continuous teaching classrooms was designed. Through the visualisation results, it can be seen that the research method can accurately detect the behaviour categories of students, and the effect is better in target dense scenes. Through quantitative analysis using correlation technology, it can be concluded that there are very few students with the lowest level of seriousness, while the majority of students have a level of seriousness above the qualified level. The overall situation of the teaching classroom is good. These student behaviour analyses can energy the level of seriousness of students, form effective and objective classroom feedback, provide teachers with objective and accurate reference value, and promote the construction of smart classrooms.

Implementation of an AI Algorithm in Clinical Practice to Reduce Missed Incidental Pulmonary Embolisms on Chest CT and Its Impact on Short-Term Survival.

A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routine. This study analyzes the radiological reports of iPE cases before and after implementation of an artificial intelligence (AI) algorithm for iPE detection. Furthermore, we investigate the anatomic distribution patterns within missed iPE cases and mortality within a 90-day follow-up in patients before and after AI use. This institutional review board-approved observational single-center study included 5298 chest computed tomography scans performed for reasons other than suspected pulmonary embolism (PE). We compared 2 cohorts: cohort 1, consisting of 1964 patients whose original radiology reports were generated before the implementation of an AI algorithm, and cohort 2, consisting of 3334 patients whose scans were analyzed after the implementation of an Food and Drug Administration-approved and CE-certified AI algorithm for iPE detection (Aidoc Medical, Tel Aviv, Israel). For both cohorts, any discrepancies between the original radiology reports and the AI results were reviewed by 2 thoracic imaging subspecialized radiologists. In the original radiology report and in case of discrepancies with the AI algorithm, the expert review served as reference standard. Sensitivity, specificity, prevalence, negative predictive value (NPV), and positive predictive value (PPV) were calculated. The rates of missed iPEs in both cohorts were compared statistically using STATA (Version 17.1). Kaplan-Meier curves and Cox proportional hazards models were used for survival analysis. In cohort 1 (mean age 70.6 years, 48% female [n = 944], 52% male [n = 1020]), the prevalence of confirmed iPE was 2.2% (n = 42), and the AI detected 61 suspicious iPEs, resulting in a sensitivity of 95%, a specificity of 99%, a PPV of 69%, and an NPV of 99%. Radiologists missed 50% of iPE cases in cohort 1. In cohort 2 (mean age 69 years, 47% female [n = 1567], 53% male [n = 1767]), the prevalence of confirmed iPEs was 1.7% (56/3334), with AI detecting 59 suspicious cases (sensitivity 90%, specificity 99%, PPV 95%, NPV 99%). The rate of missed iPEs by radiologists dropped to 7.1% after AI implementation, showing a significant improvement (P < 0.001). Most overlooked iPEs (61%) were in the right lower lobe. The survival analysis showed no significantly decreased 90-day mortality rate, with a hazards ratio of 0.95 (95% confidence interval, 0.45-1.96; P = 0.88). The implementation of an AI algorithm significantly reduced the rate of missed iPEs from 50% to 7.1%, thereby enhancing diagnostic accuracy. Despite this improvement, the 90-day mortality rate remained unchanged. These findings highlight the AI tool's potential to assist radiologists in accurately identifying iPEs, although its implementation does not significantly affect short-term survival. Notably, most missed iPEs were located in the right lower lobe, suggesting that radiologists should pay particular attention to this area during evaluations.

Car detection algorithm in urban based on YOLOv5

Car detection is essential to promote the development of intelligent technologies such as autonomous driving and car forward collision warning. To deal with low detection accuracy, poor real-time performance and robustness of existing object detection algorithms, a YOLOv5-based car detection algorithm is used. The advanced structure design of Backbone, Neck and Head in YOLOv5 demonstrates powerful feature extraction and multi-scale object detection capabilities. The detection results reveal that the algorithm is able to quickly recognize cars in images in different urban scenes, and the detection accuracy can reach more than 95%, which can satisfy the application demands for real-time car detection.

High-precision and lightweight small-target detection algorithm for low-cost edge intelligence

The proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50–95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.

YOLOv9-YX: lightweight algorithm for underwater target detection

YOLOv9-YX: lightweight algorithm for underwater target detection

A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model.

CECS-CLIP: Fusing Domain Knowledge for Rare Wildlife Detection Model

Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&amp;F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field.

Defective Pennywort Leaf Detection Using Machine Vision and Mask R-CNN Model

Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance.