Proposed Detection Method Research Articles

Potholes Detection in Doppler Radar Signal’s Power Spectral Density using Decision Tree

Potholes are defects on the surface of roads, streets, or pavements brought on by depressions or holes, which are hazardous for vehicles and pedestrians, whether small divots or huge craters. Various methods have been explored to improve the accuracy of potholes detection. Existing approaches have advantages and disadvantages. This paper presents the proposed method of pothole detection utilising Doppler Radar signal's Power Spectrum Density (PSD) together with the Decision Tree classification algorithm. While continuous waveform (CW) radar is able to identify moving targets, it cannot localise the exact depth of the reflector, which is the prominent characteristic of potholes. In addition, the target's reflected signal is likely to be masked by nearby harmonics. Since the radar is moving despite the target of interest, mounting it on a moving vehicle offers a different perspective. This paper explores the potential of Doppler radar's signal for pothole detection while comparing two Machine Learning (ML) techniques. A commercially over-the-shelf (COTS) K-LC2 Doppler radar was employed to acquire pothole and non-pothole raw datasets. Doppler signal was hardly distinguished between pothole and non-pothole, either in the time or frequency domain. Hence, Doppler signals were converted to power spectral density (PSD), and PSD's features were extracted. Extracted features were applied with the coarse Decision Tree (DT) and K-Nearest Neighbours (KNN) classification algorithms. The result exhibits a better accuracy of 91.2% for 80:20 distribution by using the Decision Tree.

WSS-YOLO: An improved industrial defect detection network for steel surface defects

Surface defects in steel manufacturing can compromise product quality and safety. Detecting these diverse and complex defects under industrial conditions is challenging. This paper proposes WSS-YOLO, a YOLOv8-based model, for accurate defect detection on industrial steel. Firstly, the dynamic non-monotonic focusing mechanism based on WIoU loss was employed to focus on anchor boxes with ordinary quality, thereby improving the overall performance of the detector. Secondly, the C2f-DSC module based on dynamic snake convolution is designed to enable the model to adaptively adjust the receptive field. Finally, GSConv and VOV-GSCSP modules are introduced into the neck network to reduce the computational complexity and parameter quantity while ensuring the accuracy of the model. This paper conducts extensive experiments on the public datasets NEU-DET and GC10-DET, achieving mAP of 82.3% and 72.0%, respectively, outperforming other excellent models. Moreover, the effectiveness of the proposed method in industrial defect detection is also validated.

ANALYSIS OF SURFACE QUALITY IN BASE ACRYLIC PLASTIC USING FLAW DETECTION METHOD

Before scanning the surface of the base acrylic plastic, ten samples were selected with a surface that had undergone all stages of finishing according to the proper dental protocol and ten samples that had previously been artificially damaged with scratches using sandpaper. This study addresses the assessment of the quality of removable orthopedic appliances concerning surface polishing and detection of microdefects. The research employs a comparative analysis of reference samples of base plastic and artificially damaged samples using flaw detection analysis. Prior to scanning the surface of the base acrylic plastic, ten samples underwent all stages of finishing according to dental protocol, while another ten samples were intentionally damaged with scratches using sandpaper. The macrophotographs obtained were processed using the "Image J" program, allowing for quantitative analysis of images, enabling the evaluation of surface quality through various parameters. This included evaluating the total number of defects, as well as the average area and perimeter of the defects. Statistical analysis conducted in the JUSP program enabled comparison between the two groups of samples, identifying significant quantitative differences. Based on the prominent indicators available in the Image J program, conclusions were drawn regarding the quality of the acrylic plastic surface and the factors influencing its condition through specific mechanical defects. The proposed method of light-optical flaw detection expands the clinical assessment capabilities of removable orthopedic structures (base plastic), particularly concerning potential bacterial contamination, which can serve as a significant factor in the formation of inflammation foci on the mucous membrane of the denture-supporting oral tissues. Flaw detection analysis enables a comprehensive assessment of material surface polishing quality and the detection of surface defects that may arise during production or use, thus preventing biofilm accumulation. The experiment demonstrated the effectiveness of light-optical flaw detection and highlights the potential for its further improvement and broader implementation in clinical practice.

Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos.

Deep learning techniques have been investigated for the computer-aided diagnosis of thyroid nodules in ultrasound images. However, most existing thyroid nodule detection methods were simply based on static ultrasound images, which cannot well explore spatial and temporal information following the clinical examination process. In this paper, we propose a novel video-based semi-supervised framework for ultrasound thyroid nodule detection. Especially, considering clinical examinations that need to detect thyroid nodules at the ultrasonic probe positions, we first construct an adjacent frame guided detection backbone network by using adjacent supporting reference frames. To further reduce the labour-intensive thyroid nodule annotation in ultrasound videos, we extend the video-based detection in a semi-supervised manner by using both labeled and unlabeled videos. Based on the detection consistency in sequential neighbouring frames, a pseudo label adaptation strategy is proposed for the refinement of unpredicted frames. The proposed framework is validated on 996 transverse viewed and 1088 longitudinal viewed ultrasound videos. Experimental results demonstrated the superior performance of our proposed method in the ultrasound video-based detection of thyroid nodules.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic mode decomposition with control is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k ) is input into the k-nearest neighbor classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and Vk (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k , and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.

Detection of Land Surface Temperature anomalies using ECOSTRESS in Olkaria geothermal field

Geothermal systems can be used to produce low-emission energy throughout the day and night, regardless of the weather conditions. These features make geothermal systems a sustainable and reliable energy source, which can be exploited on a much larger scale than it is now. Remote sensing techniques can support detecting areas potentially suitable for geothermal energy production, thereby reducing the costs of preliminary exploration. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can provide nighttime thermal imagery, which can be used for geothermal anomaly detection. This paper presents a method for automated detection of geothermal anomalies using nighttime ECOSTRESS data of the study area in Olkaria, Kenya. The proposed detection method is a kernel-based one, and includes adaptions of kernel size for the cases of large geothermal anomalies. The accuracy of the method is verified with reference data acquired during field work. A producer’s accuracy of 82% is achieved, which is on average 56% points better than in randomised anomaly maps. The possible sources of errors in detection are heat capacity of surfaces, terrain features and vegetation masking the thermal signatures. The high producer’s accuracy proves potential for application in global mapping of geothermal anomalies.

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance

With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive detectors for localizing the spatial location of pedestrians, thus providing instruction for evacuation and rescue. In this paper, a novel domain adaptive subterranean 3D pedestrian detection method is proposed to adapt pre-trained detectors from the annotated road scenes to the unannotated subterranean scenes. Specifically, an instance transfer-based scene updating strategy is designed to update the subterranean scenes by transferring instances from the road scenes to the subterranean scenes, aiming to create sufficient high-quality pseudo labels for fine-tuning the pre-trained detector. In addition, a pseudo label confidence-guided learning mechanism is constructed to fully utilize pseudo labels of different qualities under the guidance of confidence scores. Extensive experiments validate the superiority of our proposed domain adaptive subterranean 3D pedestrian detection method.

A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.

A Clutter Identification and Removal Method Based on Long Delay Lines and Cross-Correlation in Through-Wall Detection

Life detection is important in earthquake rescue, but weak vital signal is susceptible to interference by clutters. Due to the undesirable characteristics of the hardware, there are two main types of clutter generated by the frequency modulation continuous wave (FMCW) radar when transmitting signals. One is generated by periodic nonlinearity during frequency modulation, and the other is generated by the phase-locked loop spuriousness (PLLS) in frequency division. They cause additional beat frequencies to appear beside those from the target, leading to false alarms. Since the suppression measures for them are different, it is necessary to distinguish the types of clutter and choose appropriate suppression methods. In this paper, the accurate theoretical modeling of the effects of the periodic nonlinearity and phase-locked loop spuriousness on the beat signal is performed to determine distinctions between them. The clutter occurring in the system used is identified as originating from phase-locked loop spuriousness through fiber-optic experiments. A method using long delay lines and cross-correlation is proposed to identify and remove it. In experiments, the false alarm rate is reduced from over 50 percent to nearly 0 percent, providing strong evidence for the effectiveness of the proposed method in through-wall detection.

Detection, differentiation and localization of replay attack and false data injection attack based on random matrix

Replay attack and false data injection attack (FDIA) are two common types of cyber-attacks against supervisory control and data acquisition systems, aiming to disrupt the normal operation of the power system by falsifying meter measurements. In this paper, we proposed a systematic methodology to defend hybrid attack with both replay attack and FDIA. Specifically, we propose a detection method applying random matrix theory to: (1) detect the hybrid attack on static state estimation, and (2) distinguish FDIA from replay attack as well as localize falsified measurements. Firstly, short-term forecast on load and renewable power generation is conducted to obtain the predicted measurements. Secondly, random variables are calculated by differentiating the forecasting measurements and real-time measurements. A random matrix is consequently constructed with the above random variables. Thirdly, hybrid attacks are detected by the changes of the linear eigenvalue statistics of the random matrix obtained by the sliding time window. More importantly, a novel multi-label classifier to distinguish replay attack from FDIA is designed to localize FDIA by combining SVD decomposition and eigenvalue analysis with convolutional neural network (SVD-CNN). Finally, comprehensive simulations on the IEEE 14-bus system and IEEE 57-bus system are provided to validate the performance of the proposed method. It is shown that the proposed detection method has strong detection ability by filtering measurement noise. Moreover, the proposed SVD-CNN improves the accuracy in FDIA localization.

Microwave extraction and molecular imprinted polymer isolation of bergenin applied to the dendrochronological chemical study of Peltophorum dubium

This study describes methodologies for extracting and isolating bergenin, a C-glucoside of 4-O-methylgallic acid found in some plants and it presents various in vitro and in vivo biological activities. Bergenin was previously obtained from the Pelthophorum dubim (Fabaceae) roots with a good yield. Conventional chromatographic procedures of the CHCl3 soluble fraction of the MeOH extract gave 3.62% of this glucoside. An HPLC/DAD method was also developed and validated for bergenin and its precursor, gallic acid quantifications. Microwave extractions with different solvents were tested to optimize the extraction of bergenin, varying the temperature and time. MAE (Microwave Assisted Extraction) was more efficient than conventional extraction procedures, giving a higher yield of bergenin per root mass (0.45% vs. 0.0839%). Molecularly imprinted polymer (MIP) and non-imprinted polymer (NIP) based on bergenin as the template molecule, methacrylic acid, and ethylene glycol dimethacrylate were synthesized and characterized by FTIR and SEM (Scanning Electron Microscopy). Bergenin adsorption experiments using MIP and NIP followed by molecular imprinted solid phase extraction (MISPE) showed that MIP had a higher selectivity for bergenin than NIP. A dendrochronological study using the proposed method for detection and quantification of gallic acid and bergenin in five P. dubium growth rings of a 31-year-old heartwood and in the phelloderm and barks indicated that bergenin was more abundant in the 11–14th growth rings of the heartwood and decreased from the heartwood to the barks.

Optimized Convolutional Forest by Particle Swarm Optimizer for Pothole Detection

Poor road maintenance leads to potholes on the road. Potholes are responsible for road accidents and even deaths in developed and developing countries. Detecting and filling road potholes is an essential part of road maintenance. Sustaining a reliable and safe road for communication depends on pothole detection. This study presents a novel combination of a convolutional neural network and an optimized machine-learning model by a heuristic algorithm for pothole detection. The proposed method comprises a shallow convolutional neural network for feature extraction and an optimized random forest model for pothole detection. The proposed model initially uses the shallow convolutional layer to extract feature sets from input pictures. Then, the particle swarm optimizer is used to eliminate irrelevant features. Finally, a combination of random forest and a particle swarm optimizer is used for pothole detection. Particle swarm optimization indicates the best subset of the extracted feature set for final pothole detection. We added 171 pictures to the already available 665 pothole pictures to evaluate the proposed method. The test set was isolated from the training set, and we trained the model on k-fold cross-validation. The experimental result indicates 99.37% accuracy, 99.37% precision, 99.38% sensitivity, and 99.38% F1-score for discriminating potholes from roads without potholes by proposed methods. The response time of the proposed method for pothole detection is 0.02 s. The proposed method can be utilized for real-time pothole detection.

A simple indicator displacement assay ensemble for rapid and selective determination and naked-eye detection of Cu2+ and citrate through a novel diazo-based colorimetric chemosensor

ABSTRACT Citrate as a naturally occurring ion with important biological roles, especially in metabolic cycles, and as an important biomarker in the early diagnosis of many diseases, including prostate cancer, has received special attention. In the present study, a novel, sensitive, fast and simple colorimetric bisazo-based chemosensor was developed for sequential determination and naked-eye detection of Cu2+ and citrate. UV-Vis spectroscopic studies showed that Reactive Red 141 (RR141) exhibits high selectivity and sensitivity towards Cu2+ in the presence of many other cations in deionised water (10 mmol L−1 MES(2-(N-morpholino)ethanesulfonic acid)/NaOH buffer solution, pH 6.0) and complex real samples as well. The obvious colorimetric response while interaction with copper and citrate is due to complexation between RR141 and Cu2+, and then the citrate detection occurred through metal transfer from the RR141-Cu2+ towards the citrate anion releasing RR141 via an indicator displacement route. The mechanism of detection has been carefully investigated using the semi-empirical calculation method of MM2 molecular mechanics. The proposed detection method for citrate ion has a detection limit of 0.15 µmol L−1 and a satisfactory linear range of 1.92–45.96 µmol L−1, which is consistent with the desired average diagnostic concentration in prostate cancer (less than 2 mmol L−1). Our studies’ results indicate the high capability of the developed chemical sensor for quantitative and qualitative citrate detection in various complex media, including biological and clinical real samples.

Detecting Fake Online Reviews: An Unsupervised Detection Method With a Novel Performance Evaluation

ABSTRACT Fake reviews are critical issues in the online world, as they affect the credibility of e-commerce platforms and undermine consumers’ trust. Therefore, fake-review detection is of great significance. Since fake-review detection is an unsupervised problem, most existing methods and performance metrics cannot be applied. In addition, contemporary review manipulations are much more difficult to detect than before. To address these two problems, we first propose a fully unsupervised method with steps of survey research, fake-review feature analysis, fake index estimation, and fake-review selection. Fake-review features can be accurately derived from existing studies and survey research. Second, we propose a recommendation-based performance metric for evaluating fake-review detection methods. This metric differs from traditional binary classification performance metrics, as it can be used on review data with no objective review authenticity classifications. In this research, we utilize Dianping as a case study to evaluate the effectiveness of the proposed detection method and performance metric.

Abnormal traffic detection system in SDN based on deep learning hybrid models

Software defined network (SDN) provides technical support for network construction of smart cities. However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection algorithms are complex and time-consuming, so it is difficult to find abnormalities in the network in time and unable to satisfy the requirements of abnormal traffic detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection method. Firstly, it completes the rough detection of abnormal traffic in the network according to the statistical information of switch ports and then uses wavelet transform and deep learning technology to extract multi-dimensional features of all traffic data flowing through suspicious switches, so as to realize the fine detection of abnormal traffic from the surface. The experimental results show that the proposed detection method based on port information can quickly locate the source of abnormal traffic. Compared with the traditional abnormal traffic detection method in SDN, the fine detection method based on multi-dimensional features improves the accuracy by 1.7 %, the recall rate by 1.6 %, and the false positive rate by 91.3 %.

Machine Recognition of DDoS Attacks Using Statistical Parameters

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic.

MGAF‐net: Gaussian saliency features guided infrared small target detection network

AbstractRobust infrared dim small target detection under sparse features is a difficult problem in the field of small target detection. Therefore, in order to solve this problem, this paper proposes an infrared dim small target detection network based on multi‐scale Gaussian significance and attention feature fusion (MGAF)‐net. In MGAF‐net, MGAF module is proposed to extract multi‐scale Gaussian saliency features of small targets. At the same time, in order to better fuse the multi‐scale Gaussian saliency features of the target, the attention mechanism is introduced in the MGAF module to enrich the feature expression of the target. The experimental results show that the proposed detection method has higher detection accuracy and higher detection speed than the existing advanced infrared dim small target detection methods.