Detection Problem Research Articles

Using Hough transform to define track boundaries as part of machine vision by train traffic-tracking hardware and software systems

Aim. In the context of machine vision, the problem of detected object boundary definition is usually solved using semantic segmentation that requires a high computational resource. Its application increases the complexity and the cost of the implemented hardware and software systems. This paper proposes an alternative method for defining the boundary of a segmented object, i.e., a railway track, for a train traffic tracking system. Methods. Since a railway track in an image can be represented by an n-th order polynomial, it is suggested to solve the problem of track boundary detection by using approximations in the form of straight lines. It is suggested using the Hough transform to detect straight lines. The former’s parametric space will be arranged in accordance with the problem to be solved. Conclusions. The proposed approximation will allow abandoning semantic segmentation and reduce the computational complexity and load.

Identification and Application of Wave Field Characteristics of Channel Waves in Extra-Thick Coal Seams

Channel wave seismic activity often occurs with thin and medium-thick coal seams being the main target layer. To address the problem of channel wave applicability detection in extremely thick coal seams, the propagation and identification characteristics of channel waves remain the focus of research. Therefore, this paper takes the in-seam wave exploration of a 27 m extremely thick coal seam as an example and uses the staggered mesh finite difference method to construct a three-dimensional medium model for numerical simulation. An analysis of the physical parameters of coal and rock, along with the dispersion characteristics of channel waves in extra-thick coal seams, is utilized, through the Zoeppritz equation and the total reflection propagation method, to calculate the imaging. We found the following: (1) The dispersion areas and weak dispersion areas along the detection direction are extremely thick coal seams. (2) There are apparent channel waves in extra-thick coal seams, with a waveform similar to body waves; the length of the wave train is shorter than that of the conventional channel wave, and the arrival time can be estimated accurately. The amplitude of the apparent channel wave is affected by the degree of dispersion, with lower attenuation and higher resolution. The characteristic of total reflection in extremely thick coal seams is that the incident angle is equal to the critical angle, and the dispersion characteristics are weak. (3) The channel waves with weak dispersion characteristics in extra-thick coal seams are mainly Love-type waves.

Deep encoder-decoder networks for belt longitudinal tear detection

The belt conveyor is susceptible to longitudinal tearing, which poses a serious threat to the safety of coal mines. Traditional methods for detecting longitudinal tears have limitations such as poor image quality, limited applicability, and high hardware costs. An improved encoder-decoder network was proposed to solve the longitudinal tear detection problem. This method utilizes a line structured light system for image acquisition. The input images are downscaled using a sorting algorithm to extract the information of pixels with high grayscale values as the input feature map for the neural network. The reduced-dimensional encoder-decoder network then semantically segments the input feature map, and the resulting pixel segmentation is mapped to the location of the longitudinal tear. Finally, the position and length of the tear are calculated by back-projecting the semantic segmentation result to the world coordinate system. Experimental results demonstrate that this method effectively reduces hardware resource consumption and improves detection speed. The DICE and MIOU scores for the improved network are 97.69% and 95.47%, respectively, while the recall and precision for improved detection are 96.60% and 95.67%, respectively. Therefore, this method can successfully monitor longitudinal tear failures and ensure the safety of transportation.

Dense pedestrian face detection in complex environments

To address the problem of dense crowd face detection in complex environments, this paper proposes a face detection model named Deep and Compact Face Detection (DCFD), which adopts an improved lightweight EfficientNetV2 network to replace the backbone network of RetinaFace. A large kernel attention mechanism is introduced to address the face detection task more accurately. The backbone network, an improved efficient channel attention (ECA) mechanism, is added to further improve the algorithm performance. The feature fusion module is an improved neural architecture search feature pyramid network (NAS-FPN) that significantly improves the face detection accuracy in different scenes. To balance the training process of positive and negative samples, we use the focus loss function to replace the traditional cross-entropy loss function. In different environments, the DCFD algorithm has shown efficient face detection performance. This algorithm provides not only a feasible and effective solution for solving the problem of face detection in dense groups but also an important basis for improving the accuracy of face detection models in practical applications.

Structural Plastic Damage Warning and Real-Time Sensing System Based on Cointegration Theory.

Structural damage can affect the long-term operation of equipment. Real-time damage warning for structures can effectively avoid accidents caused by structural damage. In this paper, a real-time warning method of structural plastic damage based on the cointegration theory is proposed. This method calculates the cointegration relationship between the strain signals at different measuring points, and the stability of the strain signal relationships is also evaluated. The problem of inaccurate detection caused by the error of strain measurement and environmental influence can be eliminated by the comprehensive judgment of strain between asymmetrical measuring points. A real-time damage sensing system is developed in this paper. In order to improve the real-time and practicability of the system, this paper proposes and determines the residual warning coefficient by analyzing the proportion of the strain residuals exceeding the residual threshold. The research on this sensing system has certain value for the engineering application of damage monitoring methods.

A novel framework for concealed weapons detection using passive terahertz imaging

ABSTRACT In this work, a novel framework for non-destructive, non-intrusive and completely automatic concealed weapon detection under human clothing for entry control and security check applications using passive terahertz (THz) images has been given. The technique models the problem of concealed weapon detection as that of binary segmentation, where the pixels corresponding to concealed weapons are foreground while the remaining are background. The core idea of the proposed framework is to first oversegment the THz image into superpixels and subsequently multiple handcrafted features are extracted from each superpixel. Finally, a machine learning-based classifier is used to perform binary classification thereby classifying each superpixel as foreground or background such that each pixel in a foreground superpixel is a foreground pixel and vice-versa for background. It is worth mentioning that this technique does not require a perfect segmentation to extract features from, rather an oversegmentation suffices. Furthermore, both global features like those based on image saliency and local features like those based on intensity are extracted considering the image properties. The detailed experiments and comparative analysis confirm that the proposed technique efficiently detects concealed weapons, exhibiting superior performance compared to state-of-the-art binary segmentation methods for the purpose.

5-D spatial-temporal information-based infrared small target detection in complex environments

Recently, infrared (IR) small target detection problem has attracted increasing attention. Tensor component analysis-based techniques have been widely utilized, while they are faced with challenges such as tensor structures, background and target estimation, and real-time performance. In this paper, we propose a 5-D spatial–temporal factor-based completion model (5D-STFC) for IR small target detection. Specifically, a 5-D whitened spatial–temporal patch-tensor is constructed. Then, we devise a spatial–temporal factor-based low-rank background estimation norm and a Moreau envelope-derived sparsity estimation norm based on joint spatial–temporal knowledge. Furthermore, we establish a comprehensive completion model for component analysis. To efficiently solve this model, we design a multi-block alternating direction method of multipliers (multi-block ADMM)-based optimization scheme. Extensive experiments conducted on five real IR sequences demonstrate the superiority of 5D-STFC over nine state-of-the-art competitive methods. It can be concluded that 5D-STFC is excellent and practical in target detectability, background suppressibility, overall performance, and real-time performance.

Bayesian multiple change-points detection in autocorrelated binary process with application to COVID-19 infection pattern

Maintaining consistent model parameters in long-run experiments is challenging. We study the multiple change-points detection problem from this viewpoint for an autocorrelated sample path of a binary response variable. The probabilities of success and correlation structure are linked to explanatory or control variables using a generalized linear model. A Bayesian paradigm is introduced here to estimate the unknown number and locations of change-points and an adaptive RJMCMC algorithm is presented to generate pseudo-random samples from the posterior distributions. This approach allows researchers to incorporate the experts' ideas into the analysis of such data. The features of the proposed method are examined via some simulation studies. We also apply the presented method to COVID-19 data from four countries to detect the change-points in the infection procedures in the presence of some explanatory variables.

Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism

As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method.

Early Detection and Prediction of Pests in Field Crops Using Transfer Learning

This research study addresses the problem of early detection and prediction of pests in field crops. The primary objective of this research is to identify and distinguish pest species from an open-source dataset that includes 5,494 images across 12 classes. We developed an efficient model with a high probability of detecting pests in field crops using pre-trained models such as EfficientNetV2 and deep learning techniques. We applied hyperparameter tuning to the model to enhance its accuracy. Our proposed model is designed to detect and predict pests at an early stage, thereby preventing crop damage. Experimental results demonstrate that the performance of the proposed model is more accurate and precise compared to state-of-the-art existing studies. The F1 scores of the model for different classes of pest images are as follows: Ants 0.96, Bees 0.98, Beetles 0.97, Caterpillars 0.98, Earthworms 0.95, Earwigs 0.97, Grasshoppers 0.96, Moths 0.96, Slugs 0.97, Snails 0.99, Wasps 0.99, and Weevils 0.98. The overall accuracy of the model across all classes is 97.17. These results demonstrate the improved performance of the proposed model for early pest detection. In the agricultural sector, this model can be immensely beneficial, aiding in quick, accurate, and reliable pest detection to support decision-making processes. Identification of pest occurrence at their early stages leads to actions on interventions, which helps in reducing crop losses avoids unnecessary spraying for chemicals, and ensures sustainable eco-friendly agricultural practices. An approach like this would help in maintaining food security and economic sustainability of farmer communities.

May Small Airways Dysfunction (SAD) Play a Role in the Idiopathic Pulmonary Fibrosis (IPF) and May SAD Be a Therapeutic Target?

Small airway dysfunction (SAD) is a pathological process that affects the bronchioles and non-cartilaginous airways below 2 mm in diameter. This short review presents a link between SAD and IPF. Pathomorphological changes of small airways in fibrotic lungs are discussed. Additionally, functional abnormalities related to SAD measured by spirometry and oscillometry are presented. The problem of early detection and treatment of SAD as a procedure potentially capable of mitigating fibrosis is mentioned.

Vibration based dual-criteria damage detection method using deep neural networks in highway bridges with steel girders

This article introduces a new method for assessing the damage in steel girders of highway bridges using deep neural networks. The method uses modified forms of modal strain energy damage index and modal flexibility damage index to train the neural network and prevent unsafe decisions. The neural network is trained using damage indexes achieved from numerical simulations of the bridge with different damage scenarios. Three types of neural networks, including convolutional neural network (CNN), Long short-term memory (LSTM) neural network, and conventional artificial neural network (ANN) are compared to achieve the best performance. The proposed method overcomes previous detection problems such as false-positive indications, limited damage locations, and scenarios, and insufficient accuracy in cases with multiple damage scenarios. The results show that the proposed method and CNN can accurately identify unspecified damage locations and severity in multi-span highway bridges. The new training method of the CNN deep neural network systems overcomes some shortcomings in ANN, such as being time-consuming, having a low convergence rate, and experiencing data overfitting. Additionally, the CNN training scheme can limit the need for massive amounts of input data and increase the speed and accuracy of network training, especially in multiple damage scenarios.

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

A Study on Resolving Dynamic analysis using deep learning Framework Logical Methods

Deep learning technologies have gained a great deal of interest in recent months for demonstrating material removal capabilities in a variety of separation, categorization, as well as other machine vision activities. The majority of these deep networks are built employing convolution or completely convolution structures. Our suggested Dynamic and Static gestures System guided by a hand gesture high dimensional data methodology We use a snapshot of a human's thoracic spine to determine the person's depth and the size of the space around his or her hands. In this research, we provide a novel object-based deep-learning framework for semantic segmentation of extremely high-resolution satellite data. In specifically, we leverage object-based prior record by augmenting a fully convolutional neural network's training strategy with an anisotropic diffusion data pre-processing phase and an additional loss term. The goal of this restricted framework is to ensure that similar visual data is assigned to the same semantic class. Here, we employ intermediate steps based on traditional image processing techniques to aid in the resolution of the subsequent problem of classification, detection, or segmentation. Research shows that adding pre- and post-processing steps to a deep learning pipeline can boost model performance over using only the network. Recent advances in UQ algorithms used for deep learning are analysed, and their potential for use in relevance feedback is addressed. It also emphasizes basic research difficulties and directions linked with UQ. Quantifiably, man-made classes with more precise geometry, such as buildings, benefited the most from our strategy, particularly along object borders, indicating the developed approach's enormous potential. The purpose of this paper is to offer an overview of the approaches used inside deep learning frameworks to either appropriately prepare the input (pre-processing) or enhance the outcomes of the network output (post-processing), with an emphasis on digital pathology image analysis.

Modern methods for determining heavy metals in soil

This article discusses the problem of heavy metal detection in soil and its impact on vegetation. Based on the experience of foreign and domestic research, this article discusses global fundamental problems and challenges, modern methods of heavy metal detection, as well as prospects for further research and new challenges facing the scientific community. The aim of the study is to identify modern and established methods for the detection of heavy metals in soil, such as spectral analysis methods and reflectance spectra of plant parts. The review summarizes the results of experimental studies confirming the effectiveness of the combined sampling and spectrometry method for estimating the concentration of heavy metals in soil, as well as the feasibility of using plant reflectance spectra to measure pollution. World experience confirms the expediency of using spectral approaches to determine heavy metals in soil and analyze their impact on vegetation. The results of the research have practical application in the field of ecology, agriculture and nature protection, allow effectively controlling the level of heavy metal pollution and taking measures for its elimination.

Detection range of a passing submerged emitter of sound in ambient noisea).

An underwater acoustic detection problem is studied in which the ambient noise present at a receiver is calculated, given information describing environmental conditions, including windspeed, and the positions of nearby ships which act as sources of background noise. The signal, whose detection is sought, is narrowband and transmitted from a source that passes by the receiver along a straight track. Cumulative Probability of Detection (CPoD) is calculated along a series of tracks with increasing closest-point-of-approach distances to the receiver. Two detection ranges are analyzed, a so-called "defender" detection range and an "intruder" detection range. Both are conservative measures associated with CPoD equaling 0.5 during the transit of the submerged vessel. Predictions of the detection range are compared across independent attempts to solve the same problem with different modelling approaches. The spread of results (i.e., the "reproducibility" of the predictions) is discussed and reasons for differences are highlighted. Environmental conditions that strongly affect detection performance are discussed, as is the use of CPoD as a single-valued metric to describe detection performance.

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders.

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantine colluders' model updates from a spectral analysis lens, and, formulates the detection of Byzantine misbehaviours as a community detection problem in weighted graphs. The modified normalized graph cut is then utilized to discern attackers from benign participants. Moreover, the Spectral heuristics is adopted to make the detection robust against various attacks. The proposed Byzantine colluder resilient method, i.e., FedCut, is guaranteed to converge with bounded errors. Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model accuracy (MA) under various attacks. It was shown that FedCut's averaged MA is 2.1% to 16.5% better than that of the state of the art Byzantine-resilient methods. In terms of the worst-case model accuracy (MA), FedCut is 17.6% to 69.5% better than these methods.

The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and Data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis

This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly discriminative novelty score, which leverages the aleatoric semantic uncertainty as this is modeled in the class scores outputted by the YOLOv5-nano object detection (OD) model. To account for tissue continuity, we propose using the global scores (probability vector) when the model is applied to the entire sagittal sequence. The second module of the proposed pipeline constitutes the MINIROCKET timeseries classification model for determining whether a knee has an ACL tear. To better evaluate the generalization capabilities of our approach, we also carry out cross-database testing involving two public databases (KneeMRI and MRNet) and a validation-only database from University General Hospital of Larissa, Greece. Our method consistently outperformed (p-value<0.05) the state-of-the-art (SOTA) approaches on the KneeMRI dataset and achieved better accuracy and sensitivity on the MRNet dataset. It also generalized remarkably good, especially when the model had been trained on KneeMRI. The presented framework generated at least 2.1 times less carbon emissions and consumed at least 2.6 times less energy, when compared with SOTA. The integration of aleatoric semantic uncertainty-based scores into a novelty detection framework, when combined with the use of lightweight OD and timeseries classification models, have the potential to revolutionize the MRI-based injury detection by setting a new precedent in diagnostic precision, speed and environmental sustainability. Our resource-efficient framework offers potential for widespread application.

Real-Time Detection and Counting of Wheat Spikes Based on Improved YOLOv10

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model’s feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research.