Traditional Detection Methods Research Articles

Weather-Domain Transfer-Based Attention YOLO for Multi-Domain Insulator Defect Detection and Classification in UAV Images.

Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field.

End-Edge-Cloud Collaboration-Based False Data Injection Attack Detection in Distribution Networks

False data injection attack (FDIA) can pose a severe threat to the distribution networks (DN), and the accurate detection of FDIA plays a key role in the safe and reliable operation of the DN. In this paper, an end-edge-cloud collaboration-based detection framework is proposed to detect FDIA in the DN. First, in order to effectively preserve the privacy of different stakeholders in the DN and solve the problem of data island, a federated learning (FL)-based edge-cloud collaboration mechanism is designed according to the proposed end-edge-cloud collaboration framework to jointly train the local FDIA detection models and eventually build a comprehensive FDIA detection model. Then, considering the temporal-spatial correlation of measurement data, a local data-driven FDIA detection model is proposed based on a novel temporal-spatial graph convolutional network (TSGCN) which can extract temporal-spatial features of the measurement data and improve the FDIA detection performance. In general, compared with the traditional centralized FDIA detection methods, the proposed method can make full use of the computational capacity of distributed edge devices and reduce the pressure of computation on the control centre. Finally, simulation results based on the modified IEEE 14- bus and IEEE 118- bus distribution systems indicate that the proposed method can effectively improve the accuracy of FDIA detection compared with other methods.

A novel approach for detecting deep fake videos using graph neural network

Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.

A Semi-Supervised Multi-Scale Deep Adversarial Model for Fan Anomaly Detection

Failure of cooling fan will have a great impact on the operation of the air-conditioning cooling system. Traditional anomaly detection methods for fans rely on manual feature extraction, which is easy to lose effective information. Deep learning-based methods have potential to overcome such a problem, however, it is difficult to train the network without enough monitoring data. In operation, cooling usually worked at high speed, and its monitoring data contain much high-speed rotational noise. This will confuse the networks to focus on high-energy noise while lose the weak but useful information. To solve these problems, we propose a semi-supervised multi-scale deep adversarial model (SMDAM) for fan anomaly detection. In SMDAM, we propose the Linear Spectral Line Elimination (LSLE) technique to reduce the interference of rotational noise spectral lines on network training. Subsequently, a multi-scale feature extraction module is introduced to enrich diagnostic information, which extracts features from different scales without increasing the depth of the convolutional layer. Finally, two engineering data sets are used to verify the superiority of the proposed method. The results show that SMDAM is superior to other methods.

An intelligent water supply pipeline leakage detection method based on SV-WTBSVM

Water supply pipeline leakage not only wastes resources but also causes dangerous accidents. Therefore, detecting the state of pipelines is a critical task. With the expansion of the scale of the water supply pipeline, the amount of data collected by the leak detection system is gradually increasing. Moreover, there is an imbalance of sample in the data. This makes the detection performance of traditional leakage detection methods deteriorate. To solve the above issues, this paper proposes a pipeline leakage intelligent detection method based on a support vector weighted twin-bound support vector machine (SV-WTBSVM). Noise in the data negatively affects the performance of the classifier. To eliminate the effect of noise, a hybrid denoising algorithm based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used for denoising to filter out the noise in the data. Twin bound support vector machine (TBSVM) is a classical classification algorithm that has been widely used in the detection of pipeline leakage. To solve the decrease in classification accuracy caused by sample imbalance, the SV-WTBSVM algorithm oversamples the minority class samples based on the distance density and integrally undersamples the majority class samples to obtain a balanced sample. Since pipelines often have multiple working conditions, the SV-WTBSVM used for binary classification cannot meet this requirement, and this paper combines the SV-WTBSVM with the ‘one-to-one’ strategy to address the multi-classification problem. Finally, experiments have verified that the SV-WTBSVM algorithm not only retains the advantages of fast training speed and simple operation of the TBSVM but also improves the classification accuracy and generalization ability of the algorithm when dealing with imbalanced data.

Directly Self-Assembly of Aligned Ag NWs Films at the Air-Water Interface for the Detection of Pathogens in Artificial Breath Aerosols.

Exhaled aerosols from humans, containing various pathogens, are crucial for early disease diagnosis. However, the traditional pathogen detection methods, such as polymerase chain reaction, are often slow and cumbersome due to complex sampling and procedures. This study introduces a novel, direct, and label-free detection method for pathogens in respiratory aerosols, utilizing a highly aligned silver nanowire (Ag NW) film combined with a filter membrane (Ag NWs@filter) as a surface-enhanced Raman spectroscopy-active substrate. A large-scale, ordered silver nanowire film was developed through a simplified self-assembly process. This process eliminates the need for an organic phase and complex surface modifications of Ag NWs, which are common in other preparation methods. Subsequently, the fabricated Ag NWs@filter demonstrated its capability to continuously capture and efficiently preconcentrate pathogens from aerosols, achieving a remarkable detection limit of 3 × 103 CFU/mL, demonstrated using Escherichia coli (E. coli) as a model pathogen. Moreover, the classification between E. coli and Pseudomonas aeruginosa achieved an overall accuracy of 96.5% by the principal component analysis with linear discriminant analysis models. The success of this sensing strategy illustrates its potential in detecting and identifying a variety of biomarkers present in respiratory aerosols, marking a significant step forward in the field of pathogen detection.

Solid Propellant Aging Detection Method Based on Impedance Spectroscopy

Aiming at the shortcomings of large volume, high cost and long detection cycle of traditional solid propellant aging detection methods, a solid propellant aging detection method based on impedance spectroscopy is proposed. Firstly, the internal impedance of the solid propellant changes after aging, and a portable solid propellant impedance spectrum acquisition system based on impedance spectroscopy is designed based on the principle of electrochemical impedance spectroscopy, and the real and imaginary parts of the impedance spectrum are obtained. Secondly, in order to reduce the data dimension of the classification algorithm, the KPCA (Nuclear Principal Component Analysis) feature extraction algorithm is used to extract the impedance spectrum features of the solid propellant. Then, according to the impedance spectrum characteristics, the BP neural network is used for classification training, and the correspondence between the impedance spectrum and the aging time is obtained. Finally, the feasibility and effectiveness of the proposed method are verified on the physical platform, and the results show that the proposed method has the advantages of high precision and accurate classification, and can effectively predict the aging degree of solid propellant.

Anti-Interference Bottom Detection Method of Multibeam Echosounders Based on Deep Learning Models

Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0°, 35°, and 60° and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions.

CLOP-hERG: The Contrastive Learning Optimized Pre-Trained Model for Representation Learning in Predicting Drug-Induced hERG Channel Blockers

During drug development, ensuring that drug molecules do not block the hERG (human Ether-à-go-go-Related Gene) channel is critical. If this channel is blocked, it can cause many cardiovascular-related diseases. However, traditional experimental detection methods are expensive and time-consuming. In this work, we proposed a novel deep learning framework CLOP-hERG, that combines contrastive learning with the RoBERTa pre-trained model to predict whether drug molecules will block the hERG channel. We employed data augmentation techniques on molecular structures to ensure that our model can capture the multifaceted information of the molecules. Besides, we used a contrastive learning strategy to enable the model to learn meaningful molecular features from large unlabeled datasets. The RoBERTa pre-trained model played a pivotal role in this process, giving our model with a robust representational learning capability. The model, obtained through contrastive learning, was further fine-tuned to achieve high-precision prediction of hERG blockers. Through a series of experiments, we demonstrated the effectiveness of CLOP-hERG. This work provides a novel and effective strategy for predicting hERG blockers and provides some insights for other similar pharmaceutical tasks.

Deep Learning-Based Lightweight Target Detection

With the rapid development of computer vision technology, target detection techniques have also been widely used in various fields. However, traditional target detection methods usually require a large amount of computational resources and complex model structures, which pose challenges to the realization and deployment of target detection. Therefore, lightweight target detection methods arise. Lightweight target detection methods refer to reducing the computational and parametric quantities of the model as much as possible while keeping the detection accuracy unchanged. The emergence of this method can not only improve the speed and efficiency of target detection, but also make the target detection technology more adaptable to embedded devices and other resource-limited scenarios. In this paper, through the summary of the deep learning method, the impact on the accuracy and the reduction of computation is compared to give a reference to the subsequent embedded systems or small computational amount of system lightweighting.

Miniaturized detection devices powered by various heaters: A quick review under the background of water-borne disease epidemics

With the progress of society and the development of productivity, the problem of water pollution and water shortage caused by water pollution is becoming more and more serious. Water-borne diseases caused by pathogenic microorganisms also bring great harm to human beings and other life forms. These microorganisms include bacteria, viruses, protozoa, and parasitic pathogens. Traditional detection methods are time-consuming and costly, and can’t meet the needs of water resource detection. Therefore, there is an urgent need for accurate, rapid, specific, and portable detection equipment for detecting pathogenic microorganisms in water. We analyzed and sorted out the detection methods and detection equipment for detecting pathogenic microorganisms that cause water-borne diseases, summarized these detection methods and detection equipment, and analyzed the advantages and disadvantages of these detection methods. We reasonably concluded that a good detection method for pathogenic microorganisms in water should have the advantages of low cost, low energy consumption, simple operation, strong specificity, and high portability, which can more easily meet the needs of the water quality detection field. Multifunctional small nucleic acid detection devices have been reported for decades, which reduce the reaction time of nucleic acid amplification from hours to minutes, and these miniaturized devices based on nucleic acid amplification are not only highly specific but also low cost, which is very suitable for resource-limited environments.

Optical gas imaging for leak detection based on improved deeplabv3+ model

Optical gas imaging (OGI) is a technique to dynamically observe gas leaks with a wide detection range. As gasses are mobile and diffusive, traditional motion detection methods are difficult to adapt to the change of gas motion state with the environment, resulting in poor segmentation effect and low accuracy. Therefore, this paper proposes a deep learning-based semantic segmentation method of gas for optical gas imaging detection based on DeeplabV3+ model. Firstly, MobileNetV2 is used to replace the original backbone network to reduced model parameters and complexity and facilitates the edge deployment of the model. Second, the dense atrous spatial pyramid pooling (DenseASPP) module is introduced to improve the atrous spatial pyramid pooling (ASPP) module of the model to increase the model sensing field and improve the model gas segmentation accuracy. Finally, Dice loss is used to solve the sample imbalance problem. The experimental results show that IOU and F1 score of the model are improved by 4.10% and 2.47%, and it provides a new intelligent detection method for optical gas imaging.

Research on surface defect detection algorithm of pipeline weld based on YOLOv7

Aiming at the problems of low target detection accuracy and high leakage rate of the current traditional weld surface defect detection methods and existing detection models, an improved YOLOv7 pipeline weld surface defect detection model is proposed to improve detection results. In the improved model, a Le-HorBlock module is designed, and it is introduced into the back of fourth CBS module of the backbone network, which preserves the characteristics of high-order information by realizing second-order spatial interaction, thus enhancing the ability of the network to extract features in weld defect images. The coordinate attention (CoordAtt) block is introduced to enhance the representation ability of target features, suppress interference. The CIoU loss function in YOLOv7 network model is replaced by the SIoU, so as to optimize the loss function, reduce the freedom of the loss function, and accelerate convergence. And a new large-scale pipeline weld surface defect dataset containing 2000 images of pipeline welds with weld defects is used in the proposed model. In the experimental comparison, the improved YOLOv7 network model has greatly improved the missed detection rate compared with the original network. The experimental results show that the improved YOLOv7 network model mAP@80.5 can reach 78.6%, which is 15.9% higher than the original model, and the detection effect is better than the original network and other classical target detection networks.

Isotachophoretic quantification of total viable bacteria on meat and surfaces

BackgroundThe quantification of microbes, particularly live bacteria, is of utmost importance in assessing the quality of meat products. In the context of meat processing facilities, prompt identification and removal of contaminated carcasses or surfaces is crucial to ensuring the continuous production of safe meat for human consumption. The plate count method and other traditional detection methods are not only labour-intensive but also time-consuming taking 24–48 h. ResultsIn this report, we present a novel isotachophoretic quantification method utilizing two nucleic acid stains, SYTO9 and propionic iodide, for the detection of total viable bacteria. The study employed E. coli M23 bacteria as a model organism, with an analysis time of only 30 min. The method demonstrated a limit of detection (LOD) of 184 CFU mL−1 and 14 cells mL−1 for total viable count and total cell count, respectively. Furthermore, this new approach is capable of detecting the microbial quality standard limits for food contacting surfaces (10 CFU cm−2) and meat (1.99 × 104 CFU cm−2) by swabbing an area of 10 × 10 cm2. SignificanceIn contrast to the culture-based methods usually employed in food processing facilities, this isotachophoretic technique enables easy and rapid detection (<30 min) of microorganisms, facilitating crucial decision-making essential for maintaining product quality and safety.

Solar position detection method by bionic polarized light compass.

To address the needs of polarized light navigation for accurate position information of feature points in the sky, an accurate solar position detection method based on an all-sky polarization pattern imaging system is proposed. Unlike the traditional spot-based solar position detection method, this method uses the polarization information inherent in the atmosphere to accurately measure solar position. This approach is characterized by simple detection, high accuracy, and wide application range. The optical acquisition system is composed of three miniature large-field camera modules and polarizers, which enables a more compact structure, smaller size, and lesser height. Based on this principle, the solar position solution algorithm was simulated and then verified in various weather environments using the optical acquisition system built as part of this study. Solar position was detected at different moments on the same day in clear weather, and the accuracy of the measured solar altitude and azimuth angles was 0.024° and 0.03°, respectively. The accuracy of the measured solar altitude and azimuth angles was 0.08° and 0.05°, respectively, when the sun was shielded by high-rise buildings and 0.3° and 0.1° when the sun was shielded by branches and tree leaves. Aerosol concentrations exceeding a certain amount destroyed the Rayleigh distribution pattern of polarized light, thus affecting solar position detection accuracy. It is concluded that this novel detection method can not only meet the needs of polarized light navigation for solar position, but also provide a new exploration idea for enthusiasts who are eager to explore the mysteries of the universe.

Loop closure detection of visual SLAM based on variational autoencoder.

Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance.

Dense-YOLOv7: improved real-time insulator detection framework based on YOLOv7

Abstract Due to various mechanical and electrical stresses caused by changes in environmental and power load conditions, insulators may fail, resulting in serious economic losses. Manual cleaning of insulators is subject to weather and environmental constraints and poses significant safety risks. Traditional insulator detection methods have problems such as slow detection speed and poor robustness. Therefore, this paper proposes a real-time insulator detection algorithm based on the improved YOLOv7. First, in order to effectively streamline the number of parameters, Dense-YOLOv7 adopts the dense connection concept of DenseNet to design DenseBlock. Second, replacing the loss function with Focal Loss to solve the problem of unbalanced matching of foreground and background sample quantities has improved the detection accuracy of the model. Finally, to address the issue of PReLU and LeakyReLU activation functions being insensitive to spatial information, the activation function is replaced with FReLU to improve the robustness of the model. The experimental dataset used in this paper is a combination of the Chinese Power Transmission Line Insulator Dataset (CPTLID) and our own dataset. The experimental results show that the improved algorithm in this paper has only 44.23 M parameters and a detection speed of 44.87 FPS, which is 4.8% less than that of YOLOv7 and 8.14% quicker than that of YOLOv7. Experimental results show that Dense-YOLOv7 can significantly streamline the model parameter size while maintaining high accuracy and effectively improve the detection speed, which can meet the application requirements of real-time insulator detection.

Combined Filtering Method for Offshore Oil and Gas Platform Point Cloud Data Based on KNN_PCF and Hy_WHF and Its Application in 3D Reconstruction.

With the increasing scale of deep-sea oil exploration and drilling platforms, the assessment, maintenance, and optimization of marine structures have become crucial. Traditional detection and manual measurement methods are inadequate for meeting these demands, but three-dimensional laser scanning technology offers a promising solution. However, the complexity of the marine environment, including waves and wind, often leads to problematic point cloud data characterized by noise points and redundancy. To address this challenge, this paper proposes a method that combines K-Nearest-Neighborhood filtering with a hyperbolic function-based weighted hybrid filtering. The experimental results demonstrate the exceptional performance of the algorithm in processing point cloud data from offshore oil and gas platforms. The method improves noise point filtering efficiency by approximately 11% and decreases the total error by 0.6 percentage points compared to existing technologies. Not only does this method accurately process anomalies in high-density areas-it also removes noise while preserving important details. Furthermore, the research method presented in this paper is particularly suited for processing large point cloud data in complex marine environments. It enhances data accuracy and optimizes the three-dimensional reconstruction of offshore oil and gas platforms, providing reliable dimensional information for land-based prefabrication of these platforms.

Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7).

Frequent outbreaks of viral diseases have brought substantial negative impacts on society and the economy, and they are very difficult to detect, as the concentration of viral aerosols in the air is low and the composition is complex. The traditional detection method is manually collection and re-detection, being cumbersome and time-consuming. Here we propose a virus aerosol detection method based on microfluidic inertial separation and spectroscopic analysis technology to rapidly and accurately detect aerosol particles in the air. The microfluidic chip is designed based on the principles of inertial separation and laminar flow characteristics, resulting in an average separation efficiency of 95.99% for 2 μm particles. We build a microfluidic chip composite spectrometer detection platform to capture the spectral information on aerosol particles dynamically. By employing machine-learning techniques, we can accurately classify different types of aerosol particles. The entire experiment took less than 30 min as compared with hours by PCR detection. Furthermore, our model achieves an accuracy of 97.87% in identifying virus aerosols, which is comparable to the results obtained from PCR detection.

PE-Transformer: Path enhanced transformer for improving underwater object detection

Traditional object detection methods cannot effectively identify underwater objects with complex backgrounds, and it is difficult to fully obtain the details of small-scale underwater targets, resulting in poor detection performance. We propose a path-augmented Transformer detection framework to address these limitations to explore the semantic details of small-scale underwater targets in complex environments. On one hand, an embedded local path detection information scheme is devised to facilitate the interaction between high-level and low-level features, thereby enhancing the semantic representation of distinctive features of small-scale underwater targets. Rich dependency relationships are established between the acquired high-level and low-level features within the CSWin-Transformer framework, thus fortifying the semantic representation during the encoding phase. Furthermore, a individualized loss function is employed to optimize and fine-tune features at various hierarchical levels. On the other hand, a detection module with flexible and adaptive point representation that is different from conventional square detection methods is designed. This module covers the underwater target from any direction, and the salient point samples in classification localization and feature selection between points realize the feature selection improves the detection accuracy of underwater objects simultaneously. We designed a new weighted loss function to encourage the network to converge better. Experimental results on open-source underwater and remote sensing images of UTDAC, RUOD, and ADios show that the proposed method outperforms other underwater object detection methods in terms of precision(P), recall(R), comprehensive evaluation index of F1-score and FPS(frames per second).