Detection Task Research Articles

Network Intrusion Detection based on LSTM

Network intrusion detection, as an important means of ensuring daily network security, its accuracy and response speed are crucial for defending against network attacks. This article explores and implements deep learning based network intrusion detection techniques, particularly the application of Long Short Term Memory (LSTM) networks in detecting network intrusion behavior. The aim is to solve the problems of gradient vanishing and exploding in traditional RNNs, improve the emergency response capability of network systems, and enhance the reliability and security of networks. The study used the KDD99 dataset to demonstrate the effectiveness of the LSTM model in network intrusion detection. The experimental results show that the constructed LSTM model achieves an accuracy of 88.01% in network intrusion detection tasks, demonstrating high accuracy and feasibility.

A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection.

Interoceptive Accuracy Enhances Deception Detection in Older Adults.

Difficulties with deception detection may leave older adults especially vulnerable to fraud. Interoception, i.e., the awareness of one's bodily signals, has been shown to influence deception detection, but this relationship has not been examined in aging yet. The present study investigated effects of interoceptive accuracy on two forms of deception detection: detecting interpersonal lies in videos and identifying text-based deception in phishing emails. Younger (18-34 years) and older (53-82 years) adults completed a heartbeat-detection task to determine interoceptive accuracy. Deception detection was assessed across two distinct, ecologically valid tasks: i) a lie detection task in which participants made veracity judgments of genuine and deceptive individuals, and ii) a phishing email detection task to capture online deception detection. Using multilevel logistic regression models, we determined the effect of interoceptive accuracy on lie and phishing detection in younger versus older adults. In older, but not younger, adults greater interoceptive accuracy was associated with better accuracy in both detecting deceptive people and phishing emails. Interoceptive accuracy was associated with both lie detection and phishing detection accuracy among older adults. Our findings identify interoceptive accuracy as a potential protective factor for fraud susceptibility, as measured through difficulty detecting deception. These results support interoceptive accuracy as a relevant factor for consideration in interventions targeted at fraud prevention among older adults.

Combining Signals for EEG-Free Arousal Detection during Home Sleep Testing: A Retrospective Study.

Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. Objectives: The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. Methods: We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. Results: The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. Conclusions: This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection.

An in-depth analysis of data reduction methods for sustainable deep learning.

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure the similarity between the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.

A novel target detection method with dual‐domain multi‐frequency feature in side‐scan sonar images

AbstractSide‐scan sonar (SSS) detection is a key method in underwater environmental security and subsea resource development. However, many detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks when using acoustic images, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering dual‐domain multi‐frequency network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, a novel method for optimizing and improving the detection sensitivity in different frequency ranges called multi‐frequency combined attention mechanism (MFCAM) is proposed. This mechanism amplifies the relevance of dual‐domain features across different channels and spaces. Moreover, we introduce a dual‐domain feature pyramid network (D2FPN) significantly augments the depth and breadth of feature information in underwater small datasets. The methods offer plug‐and‐play functionality with substantial performance enhancements. Extensive experiments are conducted to validate the efficacy of the proposed techniques, and the results showcase their state‐of‐the‐art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. The code and models will be publicly available at https://dagshub.com/estrellaww00/D2MFNet.

G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.

Exploring Generative Adversarial Networks for Augmenting Network Intrusion Detection Tasks

The advent of generative networks and their adoption in numerous domains and communities have led to a wave of innovation and breakthroughs in artificial intelligence and machine learning. Generative Adversarial Networks (GANs) have expanded the scope of what is possible with machine learning, allowing for new applications in areas such as computer vision, natural language processing, and creative AI. GANs, in particular, have been used for a wide range of tasks, including image and video generation, data augmentation, style transfer, and anomaly detection. They have also been used for medical imaging and drug discovery, where they can generate synthetic data to augment small datasets, reduce the need for expensive experiments, and lower the number of real patients that must be included in medical trials. Given these developments, we propose using the power of generative adversarial networks to create and augment flow-based network traffic datasets. We evaluate a series of GAN architectures, including Wasserstein, conditional, energy-based, gradient penalty, and LSTM GANs. We evaluate their performance on a set of flow-based network traffic data collected from 16 subjects who used their computers for home, work, and study purposes. The performance of these GAN architectures is described according to metrics that involve networking principles, data distribution among a collection of flows, and temporal data distribution. Given the tendency of network intrusion detection datasets to have a very imbalanced data distribution, i.e., a large number of samples in the “normal traffic” category and a comparatively low number of samples assigned to the “intrusion” categories, we test our GANs by augmenting the intrusion data and checking whether this helps intrusion detection neural networks in their task. We publish the resulting UPBFlow dataset and code on GitHub 1 .

Reference-then-supervision framework for infrared and visible image fusion

Infrared and visible image fusion has drawn increasing attention of researchers in recent years, wherein the complementary information between two source images is extracted to synthesize a new fusion image with richer information. Deep neural network is the latest technique to tackle various image fusion problems. However, the natural absence of ground truth makes it very challenging to optimize deep learning models. While the current methods mainly consider to uses the two source images themselves or their visual features, to provide supervision for learning, which is easy to result in the imbalance between detail preservation and brightness distribution. To boost the performance of the fusion models, it is meaningful and necessary to establish a flexible framework that can combine the advantages of existing models to produce reliable supervision for model training. In this work, we propose a novel reference-then-supervision framework, which aims to fully leverage and exploit the available favorable reference information based on the performance of existing methods and then construct high-quality reliable supervision to assist in model building. For this purpose, we design an automatic filter to produce favorable reference and devise an adaptive enhancement method to construct reliable supervision, which helps to aggregate the advantages of various existing fusion methods for yielding visually pleasing results adapting to different complex scenarios. Extensive experiments on two commonly used datasets and our built challenging test set demonstrate that our framework can greatly improve the performance of existing fusion methods. Ablation study and empirical analysis also present the efficacy of our framework design. Furthermore, the applications on downstream pedestrian detection and object tracking tasks indicate the great potential of our framework. Our code and data are publicly available at https://github.com/zhenglab/ReferenceSupervisionIVIF.

Dynamic functional connections analysis with spectral learning for brain disorder detection

Dynamic functional connections (dFCs), can reveal neural activities, which provides an insightful way of mining the temporal patterns within the human brain and further detecting brain disorders. However, most existing studies focus on the dFCs estimation to identify brain disorders by shallow temporal features and methods, which cannot capture the inherent temporal patterns of dFCs effectively. To address this problem, this study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of dFCs and further detect the brain disorders. Concretely, dCSL includes two components, dFCs estimation module and dFCs analysis module. In the former, dFCs are estimated via the sliding window technique. In the latter, the spectral kernel mapping is first constructed by combining the Fourier transform with the non-stationary kernel. Subsequently, the spectral kernel mapping is stacked into a deep kernel network to explore higher-order temporal patterns of dFCs through spectral learning. The proposed dCSL, sharing the benefits of deep architecture and non-stationary kernel, can not only calculate the long-range relationship but also explore the higher-order temporal patterns of dFCs. To evaluate the proposed method, a set of brain disorder classification tasks are conducted on several public datasets. As a result, the proposed dCSL achieves 5% accuracy improvement compared with the widely used approaches for analyzing sequence data, 1.3% accuracy improvement compared with the state-of-the-art methods for dFCs. In addition, the discriminative brain regions are explored in the ASD detection task. The findings in this study are consistent with the clinical performance in ASD.

YOLOv8-Based Drone Detection: Performance Analysis and Optimization

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

Using a signal detection approach to understand the impacts of processing fluency and efficacy on accuracy in misinformation detection.

This experiment (N = 1,019) examined how a state of processing fluency, induced through either an easy or difficult task (reading a simple vs. complex message or recalling few vs. many examples) impacted participants' ability to subsequently detect misinformation. The results revealed that, as intended, easier tasks led to higher reports of processing fluency. In turn, increased processing fluency was positively associated with internal efficacy. Finally, internal efficacy was positively related to misinformation detection using a signal detection task. This work suggests that feelings of ease while processing information can promote confidence and a more discerning style of information processing. Given the proliferation of misinformation online, an understanding of how metacognitions - like processing fluency - can disrupt the tacit acceptance of information carries important democratic and normative implications.

A lightweight detection algorithm for tooth cracks in optical images

ObjectivesCracked tooth syndrome (CTS) is one of the major causes of tooth loss, presents the problem of early microcrack symptoms that are difficult to distinguish. This paper aims to investigate the practicality and feasibility of an improved object detection algorithm for automatically detecting cracks in dental optical images. MethodsA total of 286 teeth were obtained from Sun Yat-sen University and Guangdong University of Technology, and simulated cracks were generated using thermal expansion and contraction. Over 3000 images of cracked teeth were collected, including 360 real clinical images. To make the model more lightweight and better suited for deployment on embedded devices, this paper improves the YOLOv8 model for detecting tooth cracks through model pruning and backbone replacement. Additionally, the impact of image enhancement modules and coordinate attention modules on optimizing our model was analyzed. ResultsThrough experimental validation, we conclude that that model pruning reduction maintains performance better than replacing a lightweight backbone network on a tooth crack detection task. This approach achieved a reduction in parameters and GFLOPs by 16.8 % and 24.3 %, respectively, with minimal impact on performance. These results affirm the effectiveness of the proposed method in identifying and labeling tooth fractures. In addition, this paper demonstrated that the impact of image enhancement modules and coordinate attention mechanisms on YOLOv8's performance in the task of tooth crack detection was minimal. ConclusionsAn improved object detection algorithm has been proposed to reduce model parameters. This lightweight model is easier to deploy and holds potential for assisting dentists in identifying cracks on tooth surfaces.

Contextual cueing in change detection tasks

Contextual cueing in change detection tasks

YOLO-SegNet: A Method for Individual Street Tree Segmentation Based on the Improved YOLOv8 and the SegFormer Network

In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this paper, based on a large, publicly available urban street tree dataset, proposes YOLO-SegNet for individual street tree segmentation. In the first stage of the street tree object detection task, the BiFormer attention mechanism was introduced into the YOLOv8 network to increase the contextual information extraction and improve the ability of the network to detect multiscale and multishaped targets. In the second-stage street tree segmentation task, the SegFormer network was proposed to obtain street tree edge information more efficiently. The experimental results indicate that our proposed YOLO-SegNet method, which combines YOLOv8+BiFormer and SegFormer, achieved a 92.0% mean intersection over union (mIoU), 95.9% mean pixel accuracy (mPA), and 97.4% accuracy on a large, publicly available urban street tree dataset. Compared with those of the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPNet), UNet, DeepLabv3+, and HRNet, the mIoUs of our YOLO-SegNet increased by 10.5, 9.7, 5.0, 6.8, 4.5, and 2.7 percentage points, respectively. The proposed method can effectively support smart agroforestry development.

Both Target and Distractor are Sampled Rhythmically in a Motion Detection Task

Both Target and Distractor are Sampled Rhythmically in a Motion Detection Task

FMAW-YOLOv5s: A deep learning method for detection of methane plumes using optical images

Natural gas hydrates stored in the subsurface seabed of continental margins are one of the most important carbon reservoirs on Earth. Research on natural gas hydrates is of great significance to global warming and ecological protection. Methane plumes caused by crustal dynamics are usually considered as a sign of existence of natural gas hydrates. Detection of methane plumes thus becomes the first step of cold seep research. This paper conducts comprehensive research on detection of methane plumes based on deep learning methods and optical images. First, we proposed a method of creating high quality and balanced datasets for methane plumes detection tasks using open-source videos. We then proposed a FMAW-YOLOv5s method for methane plumes detection. The FMAW-YOLOv5s method improves the traditional YOLOv5s in design of backbone network, neck network and loss function. The FMAW-YOLOv5s method can realize accurate and fast detection of methane plumes with a precision of 96.9% and FPS of 141.7. The lightweight feature of FMAW-YOLOv5s also enables the deployment in edge computing devices such as AUVs and ROVs. This research can not only promote the study of cold seep activities, but also provide meaningful insights for detection of other underwater events such as gas pipelines leakage.

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.

CP-RDM: a new object detection algorithm for casting and pouring robots

Abstract Automating the casting sector heavily relies on pivotal technology for object detection in pouring robots. A sophisticated algorithm designed to identify and locate target pouring holes in intricate casting workshops is crucial for advancing the intelligence of the casting process. However, the workshop environment for pouring is generally challenging, with uneven lighting, varying sizes of pouring holes, and significant occlusion in the target area, all impacting the accuracy of target detection tasks.
To overcome these challenges, this paper proposes enhancing the YOLOv8s algorithm for object detection in pouring robots. Firstly, to address the issue of different scales in pouring holes, a Multi-Scale Residual Channel and Spatial Information Fusion Module (MRCS) is designed to aggregate channel and spatial information, thereby enhancing the feature extraction capability of the model. The proposed enhancement is validated on the Pascal VOC dataset. Secondly, a SimAM attention mechanism is added at the end of the backbone network to focus the object detection network more on the positional region of the pouring hole. Importantly, this addition does not introduce extra parameters or computational burden to the model.
Finally, in the detection part of the model, the detection head from the RT-DETR model is introduced. This combination of real-time detection capability from YOLO and deep feature extraction capability from RT-DETR enhances the detection accuracy of the model while ensuring real-time performance. Experimental results on the updated pouring hole dataset reveal that, with only a slight increase in parameters, the proposed model achieves a 2.5% and 3.5% improvement in mAP@0.5 and F1-Score, respectively, compared to the baseline algorithm YOLOv8s. Precision (P) is enhanced by 1.8%, recall (R) by 3.5%, and PFS reaches 110, meeting the requirements for real-time pouring in pouring robots.

Initial exploration into sarcasm and irony through machine translation

In this paper, we investigate sarcasm and irony as seen through a novel perspective of machine translation. We employ various techniques for translation, comparing both manually and automatically translated datasets of irony and sarcasm. We first clarify the definitions of irony and sarcasm and present an exhaustive field review of studies on irony both from purely linguistic as well as computational linguistic perspectives. We also propose a novel evaluation metric for the purpose of evaluating translations of figurative language, with a focus on machine-translated irony and sarcasm. The constructed English and Chinese parallel dataset includes polarized content from tweets as well as forum posts, categorized by irony types. The preferred translation model, mBART-50, is identified through a thorough experimental process. Optimal translation settings and the best-finetuned model for irony are explored, with the most effective model being finetuned on both ironic and non-ironic data. We also experimented which types of irony are best suitable for training in this specific task — short microblogging messages or longer forum posts. Moreover, we compare the capabilities of a well fine-tuned mBART to a prompt-based method using the recently popular ChatGPT model, with the conclusion that the former still outperforms the latter, although ChatGPT without any training can be considered as a “good enough” ad hoc solution in the case of a lack of data for training. Finally, we verify if the translated data – either manually, or with an MT model – can be used as training data in a task of irony detection. We believe that the presented research can be expanded into languages other than the presented here Chinese and English, which together with the ability to detect various categories of irony, could contribute to deepening the understanding of figurative language, especially irony and sarcasm.