Efficient Detection Algorithm Research Articles

A fuzzy based chicken swarm optimization algorithm for efficient fault node detection in Wireless Sensor Networks.

Wireless Sensor Networks (WSN) are built with miniature sensor nodes (SN), which are deployed into the geographical location being sensed to monitor environmental conditions, which transfer the sensed physical information to the base station for further processing. The sensor nodes frequently experience node failure as a result of their hostile deployment and resource limitations. In WSN, node failure can cause a number of issues, namely Wireless Sensor Networks topology changes, broken communications links, disconnected portions of the network, and data transmission errors. An important concern of WSN is the detecting, diagnosing and recovering of sensor node failures. In the course of this effort, an effective strategy for sensor node failure detection algorithm using the Poisson Hidden Markov Model (PHMM) and the Fuzzy-based Chicken Swarm Optimization (F-CSO) is proposed for efficient detection of sensor node faults in the WSN. The proposed work offers optimal false alarm, false positive, energy consumption, detection accuracy, network lifetime, and least delay rates. Moreover, the F-CSO provides improved localization to locate the defective sensor nodes that are present in the WSN. The proposed work is implemented in the NS2 simulator with realistic simulation parameters, and the simulation results demonstrate that the proposed work is more effective in terms of 89.5% fault detection accuracy, 19.53% throughput, 8.43% energy consumption with minimum delay and less false positive rate when it is compared with other existing state-of-art systems.

A Refined and Efficient CNN Algorithm for Remote Sensing Object Detection

Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability.

LES-YOLO: efficient object detection algorithm used on UAV for traffic monitoring

Abstract The use of UAVs for traffic monitoring greatly facilitates people’s lives. Classical object detection algorithms struggle to balance high speed and accuracy when processing UAV images on edge devices. To solve the problem, the paper introduces an efficient and slim YOLO with low computational overhead, named LES-YOLO. In order to enrich the feature representation of small and medium objects in UAV images, a redesigned backbone is introduced. And C2f combined with Coordinate Attention is used to focus on key features. In order to enrich cross-scale information and reduce feature loss during network transmission, a novel structure called EMS-PAN (Enhanced Multi-Scale PAN) is designed. At the same time, to alleviate the problem of class imbalance, Focal EIoU is used to optimize network loss calculation instead of CIoU. To minimize redundancy and ensure a slim architecture, the P5 layer has been eliminated from the model. And verification experiments show that LES-YOLO without P5 is more efficient and slimmer. LES-YOLO is trained and tested on the VisDrone2019 dataset. Compared with YOLOv8n-p2, mAP@0.5 and Recall has increased by 7.4% and 7%. The number of parameters is reduced by over 50%, from 2.9 M to 1.4 M, but there is a certain degree of increase in FLOPS, reaching 18.8 GFLOPS. However, the overall computational overhead is still small enough. Moreover, compared with YOLOv8s-p2, both the number of parameters and FLOPS are significantly reduced , while the performance is similar . As for real-time, LES-YOLO reaches 138 fps on GPU and a maximum of 78 fps on edge devices of UAV.

PcMNet: An efficient lightweight apple detection algorithm in natural orchards

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

BCIDS‐IoT: A Binary Classification Intrusion Detection Scheme for Internet of Things Communication

ABSTRACTThe integration of Internet of Things (IoT) devices into daily life has exponentially increased the amount of data. In an IoT computing environment, like Smart Homes, Internet of Medical Things, Industrial Internet of things, Internet of Vehicles, and Smart Agriculture, there is a significant volume of data being exchanged between devices, servers, and users. This gives attackers a chance to launch malicious attacks on devices and associated resources. In this article, we have addressed this issue and proposed a machine learning‐based malware detection technique for the secure communication of IoT (BCIDS‐IoT). The proposed BCIDS‐IoT employs numerous algorithms for efficient detection. The benchmark UNSW‐NB15 dataset is utilized for the analysis. BCIDS‐IoT lowers false positives, maintains high detection rates, and allows for large‐scale network traffic without compromising performance. The various models, such as logistic regression, decision trees, random forests, extra trees, K‐nearest neighbors, and artificial neural network (ANNs), are utilized in the proposed BCIDS‐IoT. Metrics like precision, recall, and F1‐score are also calculated alongside accuracy. ANN surpassed all other models with an accuracy of . Finally, the proposed BCIDS‐IoT is also compared with different closely related schemes, indicating its outperformance among all.

Simple and efficient step detection algorithm for foot-mounted IMU

Abstract This paper presents a concise, efficient, and adaptive step detection algorithm based on foot-mounted inertial measurement unit sensors. The proposed method maps the temporal values of pedestrian motion and gait diversity into two variables: the distance between peaks and valleys, and the slope. Compared to traditional sliding window methods, this approach amplifies the differences between normal and abnormal steps, allowing it to adapt to various indoor activities such as fast walking, slow walking, running, jogging, standing still, and turning. By incorporating adaptive factors, it addresses the challenge of detecting steps while going up and down stairs. The proposed algorithm overcomes the limitations of traditional adaptive threshold methods that require different temporal and peak thresholds for various gait conditions. By utilizing the significant differences in distance and slope, it effectively resolves the issue of detecting steps during stationary periods. Unlike neural network-based gait classifiers, this algorithm does not need to account for multiple gait conditions, thereby simplifying the training process. Experimental results demonstrate that the algorithm achieves an average accuracy of over 99% under mixed indoor walking conditions and over 98% accuracy in long-term outdoor walking conditions.

A lightweight defect detection algorithm for escalator steps

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

Lightweight Detection Algorithm for Air-to-Ground Threat Object

Abstract In air-to-ground threat object detection, the typically strong concealment, small size, and high movement speed of threat objects often lead to challenges such as missed detections, low accuracy, and an excessive number of network parameters. An efficient lightweight detection algorithm for air-to-ground threat objects is proposed, built upon the YOLOv7-tiny framework. To address the issue of large network parameters, lightweight modules ELAN-GS and Slim-Neck are employed. Additionally, to refine the detection precision for small objects, the NWD metric and NWD loss function are introduced. Results from experiments on the DOTA dataset highlight that, compared with the original network, the proposed algorithm achieves a 33.2% reduction in parameters and a 26.3% reduction in computational cost. The small object accuracy, mAPs@0.5:0.95, is 23.7%, which is a 4.4% enhancement relative to the original network. The significant reduction in parameters and computational cost, along with the substantial improvement in small object detection accuracy, demonstrate the effectiveness of the proposed algorithm.

An Efficient Algorithm for Automated Skin Lesion Detection: A Non-Machine Learning Approach

Skin lesion detection is a very important task in medical applications such as disease diagnostic and cancer detection. This paper suggests an efficient algorithm for skin lesion detection relying on advanced image processing techniques without using machine learning or deep learning algorithms. A set of algorithms is proposed to enhance traditional image processing methods to scrutinize dermatological images, concentrating on melanoma detection. The proposed method combines color space transformation, contrast enhancement, active contour segmentation. ABCD criteria (Asymmetry, Border irregularity, Color variegation, and Diameter) is used in feature extraction. The approach is evaluated on the ISIC (International Skin Image Collaboration) 2020 dataset, accomplishing an accuracy of 84% in differentiating between malignant and benign skin lesions. The results indicate the potential of non-machine learning techniques in dermatological image analysis, providing computationally efficient and interpretable alternative to data-intensive deep learning algorithms.

APLIKASI PENERJEMAH BAHASA ISYARAT BISINDO MENGGUNAKAN METODE YOLOv5 BERBASIS MOBILE

This research aims to develop a mobile application that is able to recognize and translate BISINDO in real-time using the YOLOv5 method. YOLOv5 (You Only Look Once version 5) is an efficient and fast object detection algorithm, making it suitable for implementation on mobile devices. In developing this application, we collected and labeled a typical BISINDO dataset, then trained the YOLOv5 model to detect and recognize these characteristics. The test results show that the implemented model has high accuracy in recognizing various BISINDO characteristics. Apart from that, this application is also designed with an intuitive and easy-to-use user interface, so that users can quickly and easily communicate with people using sign language. With this application, it is hoped that it can expand communication access for people with hearing impaired and increase their social inclusion in society. Further research is needed to continue improving the app's accuracy and efficiency and adding more features that support different user needs.

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.

Csb-yolo: a rapid and efficient real-time algorithm for classroom student behavior detection

Csb-yolo: a rapid and efficient real-time algorithm for classroom student behavior detection

Superimposed Positive Sequence Impedance for Detecting Unintentional Islanding in Microgrid

Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.

An integrated DEM code for tracing the entire regolith mass movement on asteroids

ABSTRACT This paper presents a general strategy for tracking the scale-span movement process of asteroid regolith materials. It achieves the tracking of the mass movement on the asteroid at a realistic scale, under conditions of high-resolution asteroid surface topography (submeter level) and actual regolith particle sizes. To overcome the memory exponential expansion caused by the enlarged computational domain, we improved the conventional cell-linked list method so that it can be applied to arbitrarily large computational domains around asteroids. An efficient contact detection algorithm for particles and polyhedral shape models of asteroids is presented, which avoids traversing all surface triangles and thus allows us to model high-resolution surface topography. A parallel algorithm based on Compute Unified Device Architecture for the gravitational field of the asteroid is presented. Leveraging heterogeneous computing features, further architectural optimization overlaps computations of the long-range and short-range interactions, resulting in an approaching doubling of computational efficiency compared to the code lacking architectural optimizations. Using the above strategy, a specific high-fidelity discrete element method code that integrates key mechanical models, including the irregular gravitational field, the interparticle and particle-surface interactions, and the coupled dynamics between the particles and the asteroid, is developed to track the asteroid regolith mass movement. As tests, we simulated the landslide of a sand pile on the asteroid’s surface during spin-up. The simulation results demonstrate that the code can track the mass movement of the regolith particles on the surface of the asteroid from local landslides to mass leakage with good accuracy.

The equivalences of community detection methods for bipartite networks

Investigating the community structures of bipartite networks is a frequent topic of discussion in the ecological and social fields. The most widely used methods, as proposed by numerous academics from varying perspectives, include spectral graph partitioning, modularity, non-negative matrix factorization, and stochastic block model. In this paper, we demonstrate three equivalences among these four methods. i) Both Dhillon spectral graph partitioning and Barber modularity clustering are equivalent to solving for the matrix's left and right singular vectors after relaxing the discrete constraints. ii) The non-negative matrix factorization clustering is equivalent to the Dhillon spectral graph partitioning. iii) The bipartite stochastic block model is equivalent to the constraint-based NMF that uses K-L divergence as its cost function. These equivalences, obtained through rigorous mathematical derivations, will aid in the future development of efficient algorithms for community detection in bipartite networks.

Improved and Efficient Object Detection Algorithm based on YOLOv5

Improved and Efficient Object Detection Algorithm based on YOLOv5

ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model

As the application of networks permeates various aspects of daily life, maintaining network security has become a crucial challenge. A network intrusion detection system (NIDS) functions as a critical technique for securing cyberspace and has gained considerable attention. Although researchers have made significant progress in developing NIDSs, challenges still exist in high-speed networks with overwhelming network traffic. Existing methods largely focus on improving model detection accuracy and often overlook speed and computational efficiency. This oversight renders most current methods impractical for real-world high-speed network scenarios. To address this issue, we propose an innovative and efficient network intrusion detection algorithm, namely, the Bayesian gamma mixture model (GaMM) classifier. With the recently proposed extended stochastic variational inference (ESVI) framework, we introduce lower-bound approximations to the evidence lower bound (ELBO), namely, the original variational object function. An analytically tractable Bayesian estimation algorithm for a GaMM is derived through stochastic optimization of the obtained lower bound and we validate its performance and computational efficiency on three publicly available datasets (CICMalmem2022, OPCUA, and CICIDS2018). The experimental results indicate that the proposed classifier not only achieves a detection performance comparable to that of other benchmark models but also significantly reduces both the training and detection times.

A YOLO-based deep learning model for Real-Time face mask detection via drone surveillance in public spaces

Automating face mask detection in public areas is paramount to maintaining public health, especially in the context of the COVID-19 pandemic. Utilization of technologies such as deep learning and computer vision systems enables effective monitoring of mask compliance, thereby minimizing the risk of virus spread. Real-time detection helps in prompt intervention for and enforcement of the use of masks, thereby preventing potential outbreaks and ensuring compliance with public health guidelines. This method helps save human resources and makes the reinforcement of wearing masks in public areas consistent and objective. Automatic detection of face masks serves as a key tool for preventing the spread of contagious diseases, protecting public health, and creating a safer environment for every person. This study addresses the challenges of real-time face mask detection via drone surveillance in public spaces, with reference to three categories: wearing of mask, incorrect wearing of mask, and no mask. Addressing these challenges entails an efficient and robust object detection and recognition algorithm. This algorithm can deal with a crowd of multiple faces via a mobile camera carried by a mini drone, and performs real-time video processing. Accordingly, this study proposes a You Only Look Once (YOLO) based deep learning C-Mask model for real-time face mask detection and recognition via drone surveillance in public spaces. The C-Mask model aims to operate within a mini drone surveillance system and provide efficient and robust face mask detection. The C-Mask model performs preprocessing, feature extraction, feature generation, feature enhancement, feature selection, and multivariate classification tasks for each face mask detection cycle. The preprocessing task prepares the training and testing data in the form of images for further processing. The feature extraction task is performed using a Convolutional Neural Network (CNN). Moreover, Cross-Stage Partial (CSP) DarkNet53 is used to improve the feature extraction and to facilitate the model’s object detection ability. A data augmentation algorithm is used for feature generation to enhance the model’s training robustness. The feature enhancement task is performed by applying the Path Aggregation Network (PANet) and Spatial Pyramid Pooling Network (SPPNet) algorithms, which are deployed to enhance the extracted and generated features. The classification task is performed through multi-label classification, wherein each object in an image can belong to multiple classes simultaneously, and the network generates a grid of bounding boxes and corresponding confidence scores for each class. The YOLO-based C-Mask model testing is performed by experimenting with various face mask detection scenarios and with varying mask colors and types, to ensure the efficiency and robustness of the proposed model. The C-Mask model test results show that this model can correctly and effectively detect face masks in real-time video streams under various conditions with an overall accuracy of 92.20%, precision of 92.04, recall of 90.83%, and F1-score of 89.95%, for all the three classes. These high scores have been obtained despite mini drone mobility and camera orientation adjustment substantially affecting face mask detection performance.

PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning

The detection of pomegranate fruitlets is of great significance for fruit thinning and yield estimation. However, the researches on the detection of pomegranate fruitlets are rarely considered in recent years. In addition, the immature fruit detection algorithms often have a large amount of calculation, which require high computing memory and computing resources of the equipment. Based on the mentioned issue, we propose a lightweight detection method based on the improved You Only Look Once Version 8(YOLOv8) to realize the automatic detection of pomegranate fruitlets (PG-YOLO). Firstly, the lightweight network Shufflenetv2 is used to reconstruct the backbone of YOLOv8s. Secondly, the Depthwise separable convolution is used to replace the standard convolution of Neck layer. Then, the multi-head self-attention (MHSA) is used to enhance the positioning ability and feature extraction ability of the model before the backbone network outputs features. Finally, the self-built pomegranate fruitlet dataset is used to train and test the PG-YOLO model. It is shown that, the Mean Average Precision(mAP) of pomegranate fruitlets detected by PG-YOLO model is 93.4%. The optimal weight size of PG-YOLO is only 2.2 Mb, which is 89.9% less than the original YOLOv8s. In addition, the detection speed of the PG-YOLO model is 74.1% higher than the original YOLOv8s. The results demonstrate that the study significantly reduces the complexity of the model and provides a research basis for further application in mobile devices. The self-built PG-YOLO-Dataset used in this paper has been published on github: https://github.com/LforikC/PG-YOLO-Dataset.

GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation