Detection Algorithm Research Articles

CSDFormer: A cloud and shadow detection method for landsat images based on transformer

Cloud and shadow (CS) detection is crucial prerequisite for application of remote sensing images. Current deep learning-based detection algorithms mainly employ Convolutional Neural Networks (CNNs). However, the local receptive field in CNNs cannot effectively capture global contextual information, which hinders accurate characterization of the dependency between clouds and shadows. In vision Transformers, self-attention mechanisms can effectively capture the long-distance dependencies between different regions in an image. Inspired by this, this paper proposed a new CS Detection algorithm based on a Transformer, called CSDFormer. Specifically, we exclusively employed a hierarchical Transformer structure in the encoder stage to extract features of CS. Each Transformer layer contains several multi-head self-attention mechanisms for calculating pixel-wise long-distance connectivity. The designed structure enables the Transformer to better extract global context information, which helps to strengthen the comprehension of the semantic relationships between clouds and shadows. Benefiting from the global feature extraction capability of the encoder stage, we employed several simple multilayer perceptron layers for multi-scale feature map fusion and pixel classification in the decoder stage. The proposed CSDFormer was validated using 898 Landsat 8 Biome images with 512 × 512 pixels, producing an overall accuracy of 95.28 % and a mean intersection over union of 84.08 %, outperforming three state-of-the-art CNN-based algorithms. CSDFormer is consistently more accurate in detection of both clouds and shadows. Owing to the parallel computing capability of the self-attention mechanism, CSDFormer is computationally more efficient than the three CNN-based benchmark methods. For the input spectral bands, the performance of CSDFormer produced can be further enhanced with additional thermal infrared bands.

Fast Naïve Bayes classifiers for COVID-19 news in social networks

The growth of fake news has emerged as a substantial societal concern, particularly in the context of the COVID-19 pandemic. Fake news can lead to unwarranted panic, misinformed decisions, and a general state of confusion among the public. Existing methods to detect and filter out fake news have accuracy, speed, and data distribution limitations. This study explores a fast and reliable approach based on Naïve Bayes algorithms for fake news detection on COVID-19 news in social networks. The study used a dataset of 10,700 tweets and applied text pre-processing, term-weighting, document frequency thresholding (DFT), and synthetic minority oversampling techniques (SMOTE) to prepare the data for classification. The study assessed the performance and runtime of four models: gradient boosting (GDBT), decision tree (DT), multinomial Naïve Bayes (MNB), and complement Naïve Bayes (CNB). The testing results showed that the CNB model reaches the highest accuracy, precision, recall, and F1-score of approximately 92% each, with the shortest runtime of 0.55 seconds. This study highlights the potential of the CNB model as an effective tool for detecting online fake news about COVID-19, given its superior performance and rapid processing time.

FS-YOLO: Real-time Fire and Smoke Detection based on Improved Object Detection Algorithms

FS-YOLO: Real-time Fire and Smoke Detection based on Improved Object Detection Algorithms

Underwater Object Detection Algorithm based on Lightweight Structure Optimization

Underwater Object Detection Algorithm based on Lightweight Structure Optimization

Cyberattack Analysis, Detection and Prevention using Machine Learning

Abstract: In today's linked world, with the increase of cyberattacks, it is critical to have strong detection and prevention systems. This study presents an advanced approach utilizing both machine learning and deep learning algorithms for cyberattack detection and prevention. The UNSW-NB15 dataset, renowned for its comprehensive representation of diverse cyber threats, serves as the foundation for experimentation and evaluation. Several algorithms such as Random Forest, Naïve Bayes, boosting algorithms, Artificial neural networks, Support vector machine are employed where the comparative analysis focuses on evaluating the efficiency of each algorithm in terms of recall, precision, and accuracy metrics. This study enhances the development of cybersecurity defense tactics by offering valuable perspectives on the efficacy of different machine learning methods in predicting cyberattacks. Experimental findings indicate that the boosting algorithm strategy is capable of identifying and preventing cyber threats with an accuracy rate of 94%.

Breast Cancer Tumour Masks Segmentation Using Modified U-Net with Channel Attention

Abstract: A modified U-Net for biomedical image segmentation with channel attention is developed for breast cancer image segmentation. A unique step in biomedical image analysis is segmentation because it determines the accuracy of image analysis algorithms. Algorithms for accurate and early breast cancer detection in women using mammograms depend highly on the accuracy of the segmentation stage. In this thesis, in order to improve on the segmentation ability of the traditional UNet architecture, it was modified by varying the fixed kernel sizes that it was originally designed for. In this model, progressively increasing kernel sizes of 3x3, 5x5, 7x7, 9x9 and 11x11 were used, which is different from the architecture of UNet that depended on fixed 3x3 kernel sizes for all convolutional layers. The first Convolutional block of the proposed model contains two Conv2D layers with 3x3 kernel sizes, ReLU (Rectified Linear Unit) activation function, and 16 channel dimensions. A maxpooling2D layer with pool size of (2x2) was placed to downsample the image size before feeding to the next convolutional block which used the same activation function, with 32 channel dimension and 5x5 kernel size. The next convolutional block used 64 channel dimensions with 7x7 kernel size, then a 9x9 kernel size Conv2D block with 128 channel dimensions was placed before the Conv2D with upsampling. The squeeze and excitation block was placed after this convolutional layer and in-between the two convolutional blocks with 11x11 kernel sizes, to reallocate the weight of the essential features to ensure that more discriminate features are learnt from the breast cancer images to improve the overall representational strength of the network through the performance of dynamic feature recalibration of the image channels. This significantly improved the performance of the segmentation by explicitly modeling the interdependencies between channels in the convolutional layers. The increasing kernel sizes enables the model to learn more discriminate features from the images. Jaccard similarity index, F1 score, Recall, Precision, F2 score, and accuracy results were used to compare the result of the proposed model with other models such as U-Net model and Baseline model. The results shows a better performance of 0.88 for Jaccard similarity index, 0.94 for F1 score, 0.93 for recall, 0.92 for precision, 0.92 for F2 score, and an accuracy score of 0.98%. The model is good for biomedical image segmentation.

Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo

Abstract: Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its speed and accuracy in real-time object detection tasks. Leveraging a large annotated dataset of road images, we fine-tune the YOLOv8 model to specifically detect potholes. Our trained model is capable of identifying various pothole sizes and shapes, even in challenging lighting and weather conditions.

SecureNet: Network Intrusion Detection using Machine Learning and Deep Learning Techniques

Abstract: In the ever-evolving landscape of cybersecurity, the need for robust intrusion detection systems has become paramount. This paper introduces a cutting-edge intrusion detection algorithm designed to enhance network security through the integration of advanced machine learning and deep learning methodologies. The proposed algorithm capitalizes on the strengths of both paradigms to achieve a comprehensive and adaptive approach to identifying malicious activities within a network. This research focuses on enhancing network security through the development and evaluation of a novel intrusion detection system leveraging both deep learning and traditional machine learning approaches. Utilizing the NSL-KDD dataset, we employ the Long Short-Term Memory (LSTM) model, a superior version of Recurrent Neural Networks (RNNs), and the K-Nearest Neighbors (KNN) algorithm for binary and multi-class classification of network intrusion anomalies. The LSTM model excels in capturing temporal dependencies, enabling the detection of nuanced sequential patterns, while the KNN algorithm contributes to a comprehensive classification framework. Experimental results demonstrate the effectiveness of the hybrid methodology, showcasing improved accuracy, precision, and recall compared to traditional methods. This research underscores the potential of integrating deep learning and classical machine learning techniques to bolster the capabilities of intrusion detection systems in safeguarding against evolving cyber threats

A study on concept drift detection algorithms for real-world data streams

The arrival of data has changed in today's digital environment, becoming more dynamic. Dynamic data is characterized by its speed, variety, and infinite size. Data streams are one category of dynamic data. To address the issues with data streams, several strategies and AI models were developed. One such problem is concept drift, which results from changes in the data's distribution and eventually lowers the performance of the AI model. This means that regular updates to the model are required. In our work, we will analyse the performance using evaluation metrics and compare the effectiveness of the current error-based methods with window-based methods for real-world datasets.

Enhanced Road Safety through Integrated Speed Breaker, Pothole, and Lane Detection System

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

Email Spam Classifier

Communication plays a major part in everything be it proficient or individual. Because of its widespread use, accessibility, affordability, and free services, email is a popular communication tool. The rise in email-based attacks is a direct result of email protocol weaknesses as well as the growing volume of electronic commerce and financial activities. One of the main issues with today's Internet is email spam, which can financially harm businesses and bother individual consumers. On the internet, spam emails are the main problem. Spammers find it simple to send emails that are filled with spam. Our inbox is flooded with several pointless emails from spam. We receive an overwhelming volume of spam emails every day, making it difficult and time-consuming for us to distinguish between them. Spam remains a problem despite all the efforts made to eradicate it. Furthermore, even valid emails will be removed from consideration when countermeasures become excessively sensitive. Filtering is one of the key strategies among the methods created to prevent spam. This research aims to explore machine learning algorithms and their application to our data sets. The optimal algorithm for email spam detection is chosen based on its optimal precision and accuracy.

Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism

Precise and rapid detection of seatbelts is an essential research field for intelligent traffic management. In order to improve the detection precision of seatbelts and speed up algorithm inference velocity, a lightweight seatbelt detection algorithm is proposed. Firstly, by adding the G-ELAN module designed in this paper to the YOLOv7-tiny network, the optimization of construction and reduction of parameters are accomplished, and the ResNet is compressed with the channel pruning approach to decrease computational overheads. Then, the Mish activation function is utilized to replace the Leaky Relu in the neck to enhance the non-linear competence of the network. Finally, the triplet attention module is integrated into the model after pruning to make up for the underlying performance reduction caused by the previous stage and upgrade overall detection precision. The experimental results based on the self-built seatbelt dataset showed that, compared to the initial network, the Mean Average Precision (mAP) achieved by the proposed GM-YOLOv7 was improved by 3.8%, while the volume and the computation amount were lowered by 20% and 24.6%, respectively. Compared with YOLOv3, YOLOX, and YOLOv5, the mAP of GM-YOLOv7 increased by 22.4%, 4.6%, and 4.2%, respectively, and the number of computational operations decreased by 25%, 63%, and 38%, respectively. In addition, the accuracy of the improved RST-Net increased to 98.25%, while the parameter value was reduced by 48% compared to the basic model, effectively improving the detection performance and realizing a lightweight structure.

Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment Based on Multidimensional Time Series

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

The Three Hundred: Contrasting cluster galaxy density in hydrodynamical and dark matter simulations

Cluster number counts at visible and IR wavelengths will be a key cosmological probe in the next decade thanks to the Euclid satellite mission. For this purpose, the performance of cluster detection algorithms, which at these wavelengths are sensitive to the spatial distributions of the cluster galaxy members and their luminosity functions, need to be accurately characterized. Using hydrodynamical and dark-matter-only simulations, we studied a complete sample of massive clusters beyond 7 (5) times 1014 odot $ at redshift 0 (1) on a $(1.48 \ Gpc )^3$ volume. We find that the mass resolution of the current hydrodynamical simulations (1.5 times 10$^9$ M$_ odot $) is not enough to characterize the luminosity function of the sample in the perspective of Euclid data. Nevertheless, these simulations are still useful to characterize the spatial distribution of the cluster substructures assuming a common relative mass threshold for the different flavours and resolutions. By comparing with the dark-matter-only version of these simulations, we demonstrate that baryonic physics preserves significantly low-mass subhalos (galaxies), as has also been observed in previous studies with less statistics. Furthermore, by comparing the hydro simulations with higher resolution dark-matter-only simulations of the same objects and taking the same limit in subhalo mass, we find galaxy density profiles that are significantly more cuspy towards the centre of the clusters, where the low-mass substructures tend to concentrate. We conclude that using a dark-matter-only simulation may lead to some biases on the spatial distribution and density of galaxy cluster members. Based on the preliminary analysis of few high-resolution hydro simulations we conclude that a mass resolution of 1.8 times 10$^8$ h$^ $ M$_ odot $ will be needed for simulations to approach the expected magnitude limits for the Euclid survey. These simulations are currently under way.

Error Analysis of Common Power Meter Installation Faults on Three-Phase Networks

The article is concerned with the operation of electric power quality meters and analyzers when incorrectly installed on a three-phase electrical network. An analytical study of the meter’s errors under the conditions of faulty installation was carried out. All possible types of installation errors are considered according to the proposed classification, as well as possible combinations of a pair of installation faults simultaneously. Classification of installation faults is also proposed. Matrix expressions are derived that allow modeling of any type of installation faults to quantify power and symmetry measurement errors. The proposed method allows to obtain quantitative signs that can identify a specific installation fault. The results of this study can help the development of an algorithm for the automatic detection of installation faults and their correction. The analytical results were confirmed by simulation studies on a three-phase network model in the MATLAB/SIMULINK environment. Experimental results, obtained from a commercial power quality analyzer operated under controlled laboratory conditions, are also reported.

2DLIW-SLAM:2D LiDAR-Inertial-Wheel Odometry with Real-Time Loop Closure

Abstract Due to budgetary constraints, indoor navigation typically employs
2D LiDAR rather than 3D LiDAR. However, the utilization of 2D LiDAR
in Simultaneous Localization And Mapping (SLAM) frequently encounters
challenges related to motion degeneracy, particularly in geometrically similar
environments. To address this problem, this paper proposes a robust, accurate,
and multi-sensor-fused 2D LiDAR SLAM system specifically designed for indoor
mobile robots. To commence, the original LiDAR data undergoes meticulous
processing through point and line extraction. Leveraging the distinctive
characteristics of indoor environments, line-line constraints are established
to complement other sensor data effectively, thereby augmenting the overall
robustness and precision of the system. Concurrently, a tightly-coupled front-end
is created, integrating data from the 2D LiDAR, IMU, and wheel odometry, thus
enabling real-time state estimation. Building upon this solid foundation, a novel
global feature point matching-based loop closure detection algorithm is proposed.
This algorithm proves highly effective in mitigating front-end accumulated errors
and ultimately constructs a globally consistent map. The experimental results
indicate that our system fully meets real-time requirements. When compared
to Cartographer, our system not only exhibits lower trajectory errors but also
demonstrates stronger robustness, particularly in degeneracy problem. We open
source our methods here: https://github.com/LittleDang/2DLIW-SLAM.
Keywords: SLAM,2D LiDAR, IMU, wheel odometry, tightly coupled odometry, loop
detection, optimization.

Artemisia Frigida Distribution Mapping in Grassland with Unmanned Aerial Vehicle Imagery and Deep Learning

Artemisia frigida, as an important indicator species of grassland degradation, holds significant guidance significance for understanding grassland degradation status and conducting grassland restoration. Therefore, conducting rapid surveys and monitoring it is crucial. In this study, to address the issue of insufficient identification accuracy due to the large density and small size of Artemisia frigida in UAV images, we improved the YOLOv7 object detection algorithm to enhance the performance of the YOLOv7 model in Artemisia frigida detection. We applied the improved model to the detection of Artemisia frigida across the entire experimental area, achieving spatial mapping of Artemisia frigida distribution. The results indicate: In comparison across different models, the improved YOLOv7 + Biformer + wise-iou model exhibited the most notable enhancement in precision metrics compared to the original YOLOv7, showing a 6% increase. The mean average precision at intersection over union (IoU) threshold of 0.5 (mAP@.5) also increased by 3%. In terms of inference speed, it ranked second among the four models, only trailing behind YOLOv7 + biformer. The YOLOv7 + biformer + wise-iou model achieved an overall detection precision of 96% and a recall of 94% across 10 plots. The model demonstrated superior overall detection performance. The enhanced YOLOv7 exhibited superior performance in Artemisia frigida detection, meeting the need for rapid mapping of Artemisia frigida distribution based on UAV images. This improvement is expected to contribute to enhancing the efficiency of UAV-based surveys and monitoring of grassland degradation. These findings emphasize the effectiveness of the improved YOLOv7 + Biformer + wise-iou model in enhancing precision metrics, overall detection performance, and its applicability to efficiently map the distribution of Artemisia frigida in UAV imagery for grassland degradation surveys and monitoring.

Advancements in Deep Learning for Autonomous Driving in Indian Road Conditions

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

SCB-YOLOv5: a lightweight intelligent detection model for athletes’ normative movements

Intelligent detection of athlete behavior is beneficial for guiding sports instruction. Existing mature target detection algorithms provide significant support for this task. However, large-scale target detection algorithms often encounter more challenges in practical application scenarios. We propose SCB-YOLOv5, to detect standardized movements of gymnasts. First, the movements of aerobics athletes were captured, labeled using the labelImg software, and utilized to establish the athlete normative behavior dataset, which was then enhanced by the dataset augmentation using Mosaic9. Then, we improved the YOLOv5 by (1) incorporating the structures of ShuffleNet V2 and convolutional block attention module to reconstruct the Backbone, effectively reducing the parameter size while maintaining network feature extraction capability; (2) adding a weighted bidirectional feature pyramid network into the multiscale feature fusion, to acquire precise channel and positional information through the global receptive field of feature maps. Finally, SCB-YOLOv5 was lighter by 56.9% than YOLOv5. The detection precision is 93.7%, with a recall of 99% and mAP value of 94.23%. This represents a 3.53% improvement compared to the original algorithm. Extensive experiments have verified that our method. SCB-YOLOv5 can meet the requirements for on-site athlete action detection. Our code and models are available at https://github.com/qingDu1/SCB-YOLOv5.

DSASPP: Depthwise Separable Atrous Spatial Pyramid Pooling for PCB Surface Defect Detection

Printed circuit board (PCB) defect detection is an important and indispensable part of industrial production. PCB defects, due to the small target and similarity between classes, in the actual production of the detection process are prone to omission and false detection problems. Traditional machine-learning-based detection methods are limited by the actual needs of industrial defect detection and do not show good results. Aiming at the problems related to PCB defect detection, we propose a PCB defect detection algorithm based on DSASPP-YOLOv5 and conduct related experiments on the PKU-Market-PCB dataset. DSASPP-YOLOv5 is an improved single-stage detection model, and we first used the K-means++ algorithm for the PKU-Market-PCB dataset to recluster the model so that the model is more in line with the characteristics of PCB small target defects. Second, we design the Depthwise Separable Atrous Spatial Pyramid Pooling (DSASPP) module, which effectively improves the correlation between local and global information by constructing atrous convolution branches with different dilated rates and a global average pooling branch. The experimental results show that our model achieves satisfactory results in both the mean average precision and detection speed metrics compared to existing models, proving the effectiveness of the proposed method.