Efficient Detection Algorithm Research Articles

Detailed Process for Developing an Efficient Anomaly Detection Algorithm for Real-Time Streaming Data in Large-Scale Industrial Systems

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

Metal gaskets are an essential component of the manufacturing industry, and detecting end-face defects is critical to ensuring their quality. However, current detection methods struggle to detect such defects due to the gaskets’ uneven end-face structure and small defect size. To address such issues, an approach called SR-HorNetYOLO has been proposed. This approach combines traditional machine vision and deep learning techniques. The visual saliency method is used to simplify the image and remove irrelevant features. YOLOv5 is then optimized in three ways: the GhostNet backbone replaces the CSPDarkNet53 backbone to reduce model weight, the self-attention mechanism and bottleneck transformer block improve the precision of detecting small targets, and the feature pyramid is improved through the HorNet network to enhance the predictive performance of the network. Finally, a classification module and position module are employed to classify and locate three-fusion proportion characteristic images. The experimental results show that the SR-HorNetYOLO method achieves a precision of 93.3% and a recall rate of 94.0%.

VAMYOLOX: An Accurate and Efficient Object Detection Algorithm Based on Visual Attention Mechanism for UAV Optical Sensors

Unmanned aerial vehicles (UAVs) have been widely used in various fields. As one of the key technologies in improving the autonomous sensing ability of UAV optical sensors, object detection has become a research focus in recent years. Since UAVs usually navigate at different vertical heights, the object scales and sensor field of view change violently, which brings a great difficulty to the optimization of the model. Moreover, when a UAV is flying at low level rapidly, it may cause the motion blur phenomenon on objects that are highly dense in position, leading to great challenge for distinction of these objects. To address the extremely tough problems discussed above, we propose an accurate and efficient object detection algorithm, namely VAMYOLOX. Based on YOLOX, we first redesigned the classification and regression loss function of the model to better conduct classification and localization under complex motion blur and dense scenes. Then we increase another prediction head to detect lots of tiny objects to ultimately improve the detection ability of the model for multi-scale objects. Finally, we redesigned the neck of the network by introducing the triplet attention module (TAM) to find attention regions in scenes with dense objects and in images that cover a large area, accordingly enhancing the features extracted by the backbone network. Extensive experiments on dataset VisDrone widely used in the research of UAV image object detection show that VAMYOLOX has achieved state-of-the-art (SOTA) performance with good interpretability in UAV optical sensors captured scenes. On VisDrone-DET-test-dev subset, the average precision (AP) of VAMYOLOX is 25.31%, outperforming the previous SOTA model (CornerNet) by 1.88%. On VisDrone-DET-val subset, the AP of our method is 29.4%, achieving highly competitive result with previous SOTA method (AMRNet). Not only that, VAMYOLOX achieves a maximum improvement of 2.72% compared to the AP of the baseline model (YOLOX). In addition, compared with other methods, our method has a significant advantage in speed and can meet the needs of different scenarios. The PyTorch code and trained models are available at https://github.com/yangyahu-1994/VAMYOLOX.

An Efficient Anti-Intrusion Detection Algorithm Based on MEP for Directional Sensor Networks

In wireless sensor networks(WSNs), the minimum exposure path (MEP) represents the worst coverage of the whole network, so the exploration of MEP is of great significance to prevent unauthorized objects from passing through the monitoring area. The traditional research on finding MEP only focuses on how to avoid actual obstacles, without considering how to avoid high coverage areas. MEP-based coverage optimization studies the scheme of directly adding nodes, which does not conform to the characteristics that WSNs nodes are difficult to add or replace once deployed. In order to effectively improve the monitoring ability of coverage area, this paper proposes an efficient anti-intrusion detection algorithm based on MEP for directional sensor networks. Firstly, in the stage of finding MEP, the relationship between ant colony path planning algorithm and MEP is constructed by using dynamic threshold strategy. Secondly, in order to find MEP more accurately, the exposure factor is added to the state transition probability formula of the original ant colony algorithm. Finally, in the coverage enhancement stage, this paper innovatively proposes a barrier coverage enhancement strategy, which constructs a barrier by adjusting the position of redundant nodes. The simulation results show that the MEP found in this paper accurately avoids the high coverage area, and the improved ant colony algorithm is better than the MEP found by the original ant colony algorithm. Compared with the baseline algorithm, the barrier coverage enhancement strategy has strong applicability and low cost, which significantly improves the network monitoring ability.

Efficient Massive Machine Type Communication (mMTC) via AMP

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and nonseparable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

Efficient Sign-Detection-Scheme Using Modular Computation Technique for the Moduli Set {2<sup>n</sup>-1, 2<sup>n</sup>, 2<sup>n</sup>+1, 2<sup>(n+1)</sup>-1, 2<sup>2n</sup>-5}

In computer arithmetic, one of the most important things to consider in hardware design is the ability of the system to detect and display numbers with their signs. This when properly managed will reduce errors and ensure hardware reliability. But interestingly, detecting and knowing the sign of a residue number during arithmetic operation is very difficult. Magnitude Comparison, Scaling and Number conversions are some of the other difficult operations in Residue Number System (RNS). Unlike the weighted number system, it is even extremely difficult to determine the sign of a number in an RNS architecture thereby hampering the full implementation RNS in general purpose computing. In this paper, an efficient sign detection algorithm for detecting the sign of a number in an RNS architecture is presented. In formulating the algorithms, X maximum, (X_max) is computed from the Dynamic Range, M=∏^k_i=1(m_i). Modular Computation Technique is employed as a converter to compute X from the residues (r₁, r₂, r₃) with respect to a given moduli set, say S= {m₁, m₂ ..., m_n}. X is positive if X-X_max<0 otherwise X is negative and the actual value is this case is computed as X-M. The moduli set {2ⁿ-1, 2ⁿ, 2ⁿ+1, 2⁽ⁿ⁺¹⁾-1, 2²ⁿ-5} is used for the system design implementation and for numerical illustrations. It is observed that the scheme effectively detects the sign of RNS numbers and theoretical analysis showed that simple hardware resources and low-power modular adders are used in the design. It is also observed that the scheme when implemented practically can help project RNS to be used in general purpose computing.

Automated target detection methodology for drill head sensor systems

A stepped-frequency continuous-wave radar system has been designed and integrated into a drill head for use on midrange horizontal directional drills. Data from the forward-looking radar are acquired, processed, and displayed in near real time. A unique, computationally efficient target detection algorithm has been developed and tested to predict the position of potential obstacles in front of the drill head. This method is called derivative imaging, and it provides distance to target and radial offset information such that the drill operator may respond accordingly. Application to traditional ground-penetrating radar systems is also demonstrated.

An efficient online peak detection algorithm for synchronized intestinal electrical stimulation and its application for treating diabetes.

Obesity is one of leading risk factors for type 2 diabetes and other types of chronic diseases. Synchronized intestinal electrical stimulation (SIES) has been explored for treating obesity and diabetes. In SIES, electrical stimulation is delivered to the small intestine in synchronization with the intrinsic intestinal myoelectrical activity (its basic rhythm is called slow wave) and therefore, the accurate detection of intestinal slow waves is critically important for SIES. The aim of this study is to detect the peaks in intestinal slow waves in real-time based on the automatic multiscale peak detection (AMPD) method. In this paper, we introduce an efficient technique for real-time detection of peaks in intestinal slow waves. The presented method is based on peak estimation of a given quasi-periodic signal using the AMPD method. This method uses a multi-scale approach to identify the peaks of the intestinal slow waves with high detection accuracy and a minimal delay. Throughout the experiments, the multi-scale technique is used to estimate the quasi-periodic signals using different signal-to-noise ratio, λ (optimal scale), and the "lag" β (number of datapoints for right hand estimation) as important performance factors. The performance of the presented method is also calculated and utilized in the comparison process for 10 datasets of the intestinal slow waves from rats at λ = 150ms and two values of β = 100ms and 150ms. The experimental results show that the presented method has good overall accuracy for online peak detection while maintaining low memory and computational complexity. Numerically, the overall accuracy is above 90%, and 98% for the rodent intestinal slow waves at a time-lag of 150ms. The developed SIES system has been applied to successfully reduce postprandial blood glucose in a rodent model of hyperglycemia. In conclusion, the developed algorithm is adequate for on-line peak detection of the intestinal slow waves; the SIES method used the developed peak detection algorithm which is effective in reducing postprandial blood glucose in a rodent model of hyperglycemia.

Object detection in hospital facilities: A comprehensive dataset and performance evaluation

Detecting objects in hospital indoor environments is critical for scene understanding and can have various applications in healthcare. Deep learning algorithms have proven to be effective in object recognition from images or videos, but the availability of annotated datasets plays a crucial role in their successful application. However, there is a shortage of datasets for object detection in hospital settings, hindering the advancement of hospital indoor object detection algorithms. In this paper, we present the Hospital Indoor Object Detection (HIOD) dataset, consisting of 4,417 images covering 56 object categories. The HIOD dataset represents the frequently encountered objects in hospitals and comprises 51,869 annotated objects. The dataset is characterized by dense annotation, with an average of 11.7 objects and 6.8 object categories per image. An object detection benchmark was established using the HIOD dataset and eight state-of-the-art object detectors. The benchmark provides a comprehensive evaluation of the performance of the selected object detectors on a large and diverse set of images of objects commonly seen in hospital environments. The results of the benchmark can be used to compare and analyze the performance of different object detectors and identify their strengths and weaknesses for use in hospital environments. In the benchmark, one-stage detectors have shown superior performance compared to two-stage detectors of similar parameter sizes. In particular, YOLOv6-L was able to attain a mean Average Precision (mAP) of 51.7% while operating at a detection speed of 255 FPS. The benchmark and dataset can serve as a valuable resource for researchers and practitioners in the field of computer vision and robotics, helping to advance the development of more effective and efficient object detection algorithms for developing automated operations in hospitals such as robotic disinfection and patient assistance.

Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion

To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm.

Fatigue State Detection for Tired Person in Presence of Driving Periods

Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Algorithms have been developed to address this issue by detecting fatigue and alerting the driver to dangerous condition. The major problem of the developed algorithms is their accuracy, well as the time required to detect fatigue status and alert the driver. The accuracy and time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data gathered from the deriver's brain Electroencephalogram (EEG) signals or from video streaming recordings of the driver behavior. This paper develops two distinct approaches, the first based on the use of machine learning classifiers and the second depends on the use deep learning models to produce a high-performance fatigue detection system. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolution Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time (TT) when compared to all other recent and efficient fatigue detection algorithms.

YOLO-LWNet: A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices.

To solve the demand for road damage object detection under the resource-constrained conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an efficient lightweight road damage detection algorithm for mobile terminal devices. First, a novel lightweight module, the LWC, is designed and the attention mechanism and activation function are optimized. Then, a lightweight backbone network and an efficient feature fusion network are further proposed with the LWC as the basic building units. Finally, the backbone and feature fusion network in the YOLOv5 is replaced. In this paper, two versions of the YOLO-LWNet, small and tiny, are introduced. The YOLO-LWNet was compared with the YOLOv6 and the YOLOv5 on the RDD-2020 public dataset in various performance aspects. The experimental results show that the YOLO-LWNet outperforms state-of-the-art real-time detectors in terms of balancing detection accuracy, model scale, and computational complexity in the road damage object detection task. It can better achieve the lightweight and accuracy requirements for object detection for mobile terminal devices.

Identification and Mitigation of the Selfish Node and Its Behavior in WMN

In Wireless Sensor Network (WSN) communication, every node transmits data packets to further nodes and spend its resources like battery power, CPU time and memory. In ideal situation all the nodes forward packets to other nodes according to their requirements .Presence of selfish nodes is a very big issue in WSNs. A selfish node doesn't forward packets and utilize to its own profit but it is hesitating using personal resources for others. If such activities occurs within most of the nodes in the network, the network is disrupted. Selfish behavior detection and punishment is an essential requirement in wireless sensor networks. In our paper we have described an efficient algorithm for detection and punishment of a selfish node and the necessary action to be taken if the node is a critical node.

Anomaly detection of I/O behaviours in HEP computing cluster based on unsupervised machine learning

Problematic I/O pattern is the major cause of low efficient HEP jobs. In a cluster with tens of thousands of job slots, locating the source of an anomalous workload is a nontrivial task. Automatic anomaly detection can largely shorten the recovery time of these situations and reduce manpower for problem diagnoses. This paper provides a data-driven approach to solve this problem. We design and implement an anomaly detection system based on Isolation Forest, a very efficient and scalable machine learning algorithm for spatial anomaly detection in high dimension space. Historical monitoring I/O patterns collected from the Lustre file system provides a sufficient statistical basis for model training and updating. Routine model updates and job tagging ensures adaptability and promptness. Web-based visualization and sorting tools facilitate the validation of model prediction. With the detection system, time spent on tracing the source of a problematic workload can be reduced from hours to minutes.

In-Situ Fish Heart-Rate Estimation and Feeding Event Detection Using an Implantable Biologger

Monitoring of physiology and behavior of marine animals living undisturbed in their natural habitats can provide valuable information about their well-being and response to environmental stressors. We focus on detecting the feeding behavior in predatory fish using implantable biologgers that record and analyze electrocardiogram (ECG) signals. We propose a novel processing pipeline for resource-constrained embedded systems that can infer higher-level information, such as heart-rate and feeding events, from the ECG signals in situ. Our main contributions are in proposing efficient event detection algorithms that can reliably detect fish feeding events from noisy heart-rate data based on the unique statistical properties of feeding-induced changes in the heart-rate. We evaluate our approaches using an in-house biologger that we surgically implant in twelve coral trout fish and use to collect data during an experiment for a period of ten weeks and show that our signal processing pipeline performs well with noisy ECG signals overall. Specifically, our heart-rate estimation algorithm achieves errors of less than one beat per minute even in scenarios where popular algorithms used by domain specialists perform poorly. Furthermore, our feeding detection algorithms offer improved accuracy compared with the state-of-the-art algorithms while requiring significantly reduced computational and energy resources. We implement the proposed heart-rate estimation and feeding detection algorithms on the biologger and evaluate the associated system overhead. The results show that our proposed heart-rate estimation and feeding detection algorithms can run in-situ on the biologger as they demand rather small computational and energy resources that can conveniently be provisioned. This work is an important first step towards developing effective tools for long-term monitoring of high-level parameters pertaining to the health and behavior of marine animals in the wild.

A Trie Based Set Similarity Query Algorithm

Set similarity query is a primitive for many applications, such as data integration, data cleaning, and gene sequence alignment. Most of the existing algorithms are inverted index based, they usually filter unqualified sets one by one and do not have sufficient support for duplicated sets, thus leading to low efficiency. To solve this problem, this paper designs T-starTrie, an efficient trie based index for set similarity query, which can naturally group sets with the same prefix into one node, and can filter all sets corresponding to the node at a time, thereby significantly improving the candidates generation efficiency. In this paper, we find that the set similarity query problem can be transformed into matching nodes of the first-layer (FMNodes) detecting problem on T-starTrie. Therefore, an efficient FLMNode detection algorithm is designed. Based on this, an efficient set similarity query algorithm, TT-SSQ, is implemented by developing a variety of filtering techniques. Experimental results show that TT-SSQ can be up to 3.10x faster than existing algorithms.

Energy efficient RANSAC algorithm for flat surface detection in point clouds

Mobile robots control systems achieve greater efficiency through the use of robust environmental analysis algorithms based on data collected from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs). These data sources provide information about control object environment in point cloud. The work of such algorithms, as a rule, is aimed at detecting the objects of interest and searching for the specified objects, as well as relocating its own position on the scene. There are many different approaches for solving object detection problem in point clouds, but most of them require high computational resources. In this work, many variations of the random sample consensus (RANSAC) method are analyzed for objects defined by a mathematical model of an analytical form. Statistical characteristics of data analysis were used to compare the methods. The results demonstrate the most energy efficient flat surface detection method that processes 60 RGB-D camera frames per second.

A mathematical modification of the WNTM associative algorithm for cognitive analysis and efficient detection of dualisms in text and video streaming data

We report on a modification of the WNTM (Word Network Topic Model) algorithm for efficient modelling of bitermes – pairs of words that frequently occur together in texts of different topics. The modified algorithm is an extension of the classical topic model and allows efficient detection and extraction of semantic relations between pairs of words. The paper presents formalized mathematical equations describing the process of modelling biterms, and also presents the results of experiments on real text data, confirming the effectiveness of the proposed approach.

An Efficient Lidar-Based Algorithm for Autonomous Vehicle's Visual Detection

An Efficient Lidar-Based Algorithm for Autonomous Vehicle's Visual Detection

An Efficient Lightweight Event Detection Algorithm for On-Site Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) aims to determine individual-appliance energy consumption with minimum cost by decomposing aggregated electricity measurements. Although important for achieving energy conservation and cost minimization, NILM requires high-frequency sampling rates to provide accurate results. This requirement significantly increases the need for storage and computational resources in the electric utility’s fog/cloud infrastructure and for bandwidth on the customer’s side. To resolve these issues, on- site disaggregation, i.e., on the monitoring device, can be employed. However, to keep device-cost low, lightweight NILM algorithms are needed. To this end, a lightweight event-detection algorithm designed to ease on- site implementation, on either software or hardware, is proposed. Event detection is the first, critical half of the well-established event-based NILM approach; it identifies appliance state changes (events). Although a few lightweight event-detection techniques, utilizing high-frequency data, have been presented in the literature, their performance is relatively low in complex-load cases. The proposed algorithm utilizes simple-to-compute features and employs multiple simple criteria to declare an event as detected and slope-coefficient inspection to identify steady states. Moreover, it can detect events with very small time difference between them. Comparisons show that its performance is superior even against more complex event-detection approaches, while its low computational cost is also verified.