Detection Network Research Articles

An effective attention and residual network for malware detection

AbstractDue to its open source and large user base, Android has emerged as the most popular operating system. Android's popularity and openness have made it a prime target for malicious attackers. Permissions have received great attention from researchers because of their effectiveness in restricting applications’ access to sensitive resources. However, existing malware detection methods based on permissions are easily bypassed by inter‐application resource access. To address these issues, we combine inter‐application resource access‐related intent features with permission features. Besides, we designed a customized convolutional neural network using two squeeze‐and‐excitation blocks to learn the inherent relationships between multi‐type features. The two basic SE blocks perform squeezing operations based on average pooling and max pooling, respectively, to compute channel‐wise attention from multiple perspectives. We designed a series of experiments based on real‐world samples to evaluate the efficacy of the proposed framework. Empirical results demonstrate that our framework outperforms state‐of‐the‐art methods, achieving an accuracy of 96.29%, precision of 97.52%, recall of 94.63%, F1‐score of 96.06% and MCC of 92.60%. These promising experimental results consistently demonstrate that AMERDroid is an effective approach for Android malware detection.

YOLO‐AFPN: Marrying YOLO and AFPN for external damage detection of transmission lines

AbstractTo better detect targets that may cause external damage to transmission lines, the authors present You Only Look Once‐Asymptotic Feature Pyramid Network (YOLO‐AFPN), a lightweight but efficient model. Firstly, the authors adopt a feature comparison strategy based on the knowledge of transmission line scenes, which facilitates increased attention to target features during the training. Secondly, the YOLOv8 detection network is built, and the backbone adds three layers of simple parameter‐free attention module, which extracts features while maintaining lightness, and improves the detection capability in complex scenarios. Then, in the feature fusion stage, an AFPN is constructed, which improves the multi‐scale target detection capability while reducing the number of model parameters by asymptotically fusing features that have small semantic gaps between neighbouring layers. When during the training process, the improved Mosaic data augmentation method is used to enhance the number of distributions of small targets, improve the robustness of the model. Finally, the improved model is validated, and the experimental results show that the improved model can achieve mean average precision of 86.1% at 6.6 MB, which is better than the original network for detection and meets the requirements for deployment on edge devices.

Fatigue Driving Detection Based on Driver Facial Temporal Sequences

Addressing the issues of low real-time performance and high false positive rates in driver fatigue detection methods based on deep learning, this paper proposes a temporal sequence Transformer-based fatigue detection method grounded in the localization of facial landmarks in drivers. Initially, the facial positions are obtained using the single-stage face detection algorithm RetinaFace. Subsequently, a lightweight GM module is designed as the principal feature extraction module for constructing a multi-scale fusion facial landmark detection network, and facial fatigue feature parameters based on temporal sequences are calculated according to the facial landmarks. Finally, a fatigue driving classification method based on temporal sequences Transformers is developed for classifying the sequences of fatigue feature parameters. Experimental results demonstrate that the inference time required for facial detection and landmark detection is merely 16.8 milliseconds, with the per-frame inference time for facial landmark detection being only 2.5 milliseconds, thus fulfilling the real-time requirements of fatigue driving detection during the feature extraction phase. A temporal sequence of fatigue feature parameters built on the NTHU-DDD dataset and the trained temporal sequence Transformer model resulted in an accuracy rate of 91.4% for the proposed method.

A Review for Forest Fire Monitoring in Wireless Sensor Networks

Due to the occurrence of global warming forest fires more frequent, the damage caused was shocking in recent years. In order to reduce this natural disaster, forest monitoring as an important part of forest fire prevention, which is also more valued by the public and adopts the wireless sensor network as a new type of monitoring methods. This system can form a large range of real-time detection network throughout the sensing nodes, provide great convenience for protective workers. However, this technology still has some drawbacks, such as the endurance ability and the accuracy of locating the fire source, so this paper analyzes the problem, and provides a solution.

GDPs-YOLO: an improved YOLOv8s for coal gangue detection

ABSTRACT In response to the issues of low detection accuracy, slow speed, excessively large models, and difficult deployment in existing coal gangue recognition algorithms, a coal gangue target detection network based on an inverted residual structure is proposed. By conducting in-depth research on advanced edge computing networks, DPsP structure and DsP structure have been devised in this paper, while incorporating GhostModule to construct the GDPs-YOLO network within the YOLOv8s. The experimental results demonstrate the superior performance of the GDPs-YOLO network compared to both the baseline network and the control network. In comparison with the YOLOv5 series, YOLOv8 series, and four advanced edge computing networks, an increase in detection accuracy for coal gangue targets by a maximum of 2.5%, 1.6%, and 3.3% is observed, respectively. The model simultaneously exhibits enhanced speed and reduced model size, with a speed increase ranging from 12.5% to 86.85% compared to the control network. The compressibility of the model ranges from 24.07% to 94%. The inference latency measures approximately 2.8 ms, while the image processing speed reaches around 357 images per second, thereby satisfying the requirements for real-time detection.

FS-YOLO: a multi-scale SAR ship detection network in complex scenes

FS-YOLO: a multi-scale SAR ship detection network in complex scenes

Signature Fraud Detection Using Deep Learning

The use of signatures for personal identification and verification is quite common. Signatures are validated for many documents such as Bank cheques and legal transactions. The necessity for effective automated solutions for signature verification has grown as signatures are now a prerequisite for both authorization and authentication in legal activities. Two images—the original signature and the test signature—are used as input in this process. To determine whether the signature is fake or not, the characteristics that were extracted are compared, and the difference in error values between them is examined. The growing digital landscape has increased the need for robust and efficient fraud detection systems. This project presents a unique approach to detecting signature fraud using deep learning techniques, specifically employing Siamese Neural Networks, implemented in Python. With a dataset comprising 2149 signature images, encompassing both genuine and fraudulent samples from Dutch users, our model demonstrates remarkable accuracy. The Siamese Neural Network architecture excels in signature verification tasks by learning to distinguish between genuine and fraudulent signatures through contrastive learning. The key achievement of this project is the exceptional accuracy levels attained during the training and validation phases. The model's training accuracy stands at an impressive 98.00%, while the validation accuracy reaches an astonishing 99.00%. These high accuracy rates are a testament to the effectiveness of Siamese Neural Networks in signature fraud detection. In a world where the security of digital signatures is paramount, this project showcases the power of deep learning and Siamese Neural Networks in safeguarding against fraudulent activities. The model's success in accurately distinguishing between authentic and forged signatures offers promising potential for enhancing security measures in various domains, including finance, legal, and document management.

Segmentation of bone surface from ultrasound using a lightweight network UBS-Net

Objective. Ultrasound-assisted orthopaedic navigation held promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it was critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B-US) images created substantial challenges in bone surface detection. In this study, we presented an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection. Approach. We presented an end-to-end lightweight UBS-Net for bone surface detection, using the U-Net structure as the base framework and a level set loss function for improved sensitivity to bone surface detectability. A dual attention (DA) mechanism was introduced at the end of the encoder, which considered both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaced the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) were combined with a two-class fusion module for the DA map. The decoding module finally completed the bone surface detection. Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It outperformed the state-of-the-art method with higher segmentation accuracy (Dice similarity coefficient: 88.76% versus 87.22%) when applied the retrospective ultrasound (US) data from 11 volunteers. Significance. The proposed UBS-Net for bone surface detection in ultrasound achieved outstanding accuracy and real-time performance. The new method out-performed the state-of-the-art methods. It had potential in US-guided orthopaedic surgery applications.

Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network

In recent years, with the rapid development of the Internet of Things, large-scale botnet attacks have occurred frequently and have become an important challenge to network security. As artificial intelligence technology continues to evolve, intelligent detection solutions for botnets are constantly emerging. Although graph neural networks are widely used for botnet detection, directly handling large-scale botnet data becomes inefficient and challenging as the number of infected hosts increases and the network scale expands. Especially in the process of node level learning and inference, a large number of nodes and edges need to be processed, leading to a significant increase in computational complexity and posing new challenges to network security. This paper presents a novel approach that can accurately identify diverse intricate botnet architectures in extensive IoT networks based on the aforementioned circumstance. By utilizing GraphSAINT to process large-scale IoT botnet graph data, efficient and unbiased subgraph sampling has been achieved. In addition, a solution with enhanced information representation capability has been developed based on the Graph Isomorphism Network (GIN) for botnet detection. Compared with the five currently popular graph neural network (GNN) models, our approach has been tested on C2, P2P, and Chord datasets, and higher accuracy has been achieved.

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

AbstractCurrent research on apron conflict detection is often limited to the interaction between the aircraft as a whole and other objects, making it difficult to accomplish targeted identification of vulnerable and high‐cost aircraft components. However, the implementation of detailed aircraft identification is of great significance to enhance the safety of airport surface operations. Based on the excellent performance of High‐Resolution Network (HRNet) in keypoint detection, a lightweight end‐to‐end keypoint detection network, namely Squeeze and Excitation High‐Resolution Network (SEHRNet), is proposed in this paper to solve the problems of HRNet's slower computation and more redundancy. First, the errors arising from coordinate transformations in the coding and decoding process are solved by an improved feature map coding and decoding process. Second, replace the BasicBlock in HRNet with the Depthwise separable convolutions based on the Squeeze‐and‐Excitation Networks, which drastically cuts the computational cost of the network. Third, the improved Bottleneck module is used to further enhance the capability of feature extraction. Experimental results prove that, based on the aircraft keypoint detection dataset, the SEHRNet proposed in this paper shows stronger applicability compared to the current mainstream networks. Compared with the original HRNet, the improved network has higher accuracy, faster speed, and lighter model for aircraft keypoint detection.

Self-supervised adversarial adaptation network for breast cancer detection

Breast cancer is the most commonly diagnosed cancer worldwide, and early detection is essential for reducing mortality rates. Digital mammography is currently the best standard for early detection, as it can assist physicians in treating the disease. However, inaccurate diagnoses from mammography are common and can lead to patients undergoing unnecessary tests and treatments. To address this challenge, deep-learning techniques have shown promising results in improving the accuracy and reliability of breast cancer detection. However, existing methods face two primary challenges: the lack of the annotated data, and the inability to adapt to new data domains. In this paper, we propose SelfAdaptNet to address these issues. Specifically, SelfAdaptNet employs self-supervised learning techniques, such as Bootstrap Your Own Latent (BYOL) and Simple Framework for Learning of Visual Representations (SimCLR), to tackle the problem of limited annotated data. Additionally, the adversarial technique is used to address the problem of domain shift. By successfully reducing domain disparities, this strategy enhances the model’s adaptability and robustness across a variety of clinical scenarios. Overall, our contributions offer a more effective and flexible approach for early breast cancer detection, and experimental results demonstrate that SelfAdaptNet can produce promising results as compared with other methods.

Automated pixel-level pavement marking detection based on a convolutional transformer

Accurate detection of pavement markings at the pixel level is crucial for enhancing traffic safety. The majority of current advanced deep-learning networks predominantly focus on localized features, neglecting the global context of pavement image. Such networks often result in discontinuous segmentation outcomes and suboptimal recovery of local details. In this paper, a robust model named C-Transformer is proposed to provide an effective solution to this challenge. The contributions of this paper primarily involve two aspects. Firstly, the proposed C-Transformer is designed to succinctly integrate convolution operations and self-attention, facilitating a comprehensive understanding of essential features. Secondly, an efficient Feed-Forward Network called Inverse Residual Feed-Forward Network is also proposed in this paper and deployed in C-Transformer to improve latent representations. Experimental results demonstrate that, compared to other state-of-the-art networks, the proposed C-Transformer achieves a performance enhancement of 0.93% in F-measure and a 1.64% improvement in Intersection-Over-Union. In particular, the robustness and effectiveness of the C-Transformer in accurate pavement marking detection are proved through field test results. This paper illustrates the feasibility of employing a hybrid Convolutional neural network-Transformer-based network for automatic robust pavement marking detection under noisy conditions.

Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments

AbstractTo solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3‐FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused‐MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi‐damage recognition proficiency for concrete bridges across varying field‐of‐view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real‐time multi‐damage detection for concrete bridges under complex environments.

Bi-directional information guidance network for UAV vehicle detection

AbstractUAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements.

Multi-scale perceptual YOLO for automatic detection of clue cells and trichomonas in fluorescence microscopic images

Vaginitis is a common disease among women and has a high recurrence rate. The primary diagnosis method is fluorescence microscopic inspection, but manual inspection is inefficient and can lead to false detection or missed detection. Automatic cell identification and localization in microscopic images are necessary. For vaginitis diagnosis, clue cells and trichomonas are two important indicators and are difficult to be detected because of the different scales and image characteristics. This study proposes a Multi-Scale Perceptual YOLO (MSP-YOLO) with super-resolution reconstruction branch to meet the detection requirements of clue cells and trichomonas. Based on the scales and image characteristics of clue cells and trichomonas, we employed a super-resolution reconstruction branch to the detection network. This branch guides the detection branch to focus on subtle feature differences. Simultaneously, we proposed an attention-based feature fusion module that is injected with dilated convolutional group. This module makes the network pay attention to the non-centered features of the large target clue cells, which contributes to the enhancement of detection sensitivity. Experimental results show that the proposed detection network MSP-YOLO can improve sensitivity without compromising specificity. For clue cell and trichomoniasis detection, the proposed network achieved sensitivities of 0.706 and 0.910, respectively, which were 0.218 and 0.051 higher than those of the baseline model. In this study, the characteristics of the super-resolution reconstruction task are used to guide the network to effectively extract and process image features. The novel proposed network has an increased sensitivity, which makes it possible to detect vaginitis automatically.

Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery.

Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe omission, low detection accuracy, and poor early warning effectiveness. In light of these issues, this paper proposes an improved YOLOX network for the rapid detection of forest fires in images captured by UAVs. Firstly, to enhance the network's feature-extraction capability in complex fire environments, a multi-level-feature-extraction structure, CSP-ML, is designed to improve the algorithm's detection accuracy for small-target fire areas. Additionally, a CBAM attention mechanism is embedded in the neck network to reduce interference caused by background noise and irrelevant information. Secondly, an adaptive-feature-extraction module is introduced in the YOLOX network's feature fusion part to prevent the loss of important feature information during the fusion process, thus enhancing the network's feature-learning capability. Lastly, the CIoU loss function is used to replace the original loss function, to address issues such as excessive optimization of negative samples and poor gradient-descent direction, thereby strengthening the network's effective recognition of positive samples. Experimental results show that the improved YOLOX network has better detection performance, with mAP@50 and mAP@50_95 increasing by 6.4% and 2.17%, respectively, compared to the traditional YOLOX network. In multi-target flame and small-target flame scenarios, the improved YOLO model achieved a mAP of 96.3%, outperforming deep learning algorithms such as FasterRCNN, SSD, and YOLOv5 by 33.5%, 7.7%, and 7%, respectively. It has a lower omission rate and higher detection accuracy, and it is capable of handling small-target detection tasks in complex fire environments. This can provide support for UAV patrol and rescue applications from a high-altitude perspective.

W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT

Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework based on deep learning techniques to screen for diseases using axial bone images and to identify the local positions of bone lesions. To address the limited availability of labeled samples, we developed a new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations. We theoretically and experimentally determined that combining the concepts of data augmentation optimized for GAN training (DAG) and Wasserstein GAN yields a considerably stable generation of synthetic images and effectively aligns their distribution with that of real images, thereby achieving a high degree of similarity. The classification model was trained using real and synthetic samples. Consequently, the GAN technique used in the diagnostic test had an improved F1 score of approximately 7.8% compared with CA. The final F1 score was 80.24%, and the recall and precision were 84.3% and 88.7%, respectively. The results obtained using the augmented samples outperformed those obtained using pure real samples without augmentation. In addition, we adopted explainable AI techniques that leverage a class activation map (CAM) and principal component analysis to facilitate visual analysis of the network’s results. The framework was designed to suggest an attention map and scattering plot to visually explain the disease predictions of the network.

Crack detection based on attention mechanism with YOLOv5

AbstractIn order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%.

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model

Background and Objective:People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. Methods:This study quantified the functional connectivity of the subject’s brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. Results:Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. Conclusions:This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.

PointeNet: A lightweight framework for effective and efficient point cloud analysis

The conventional wisdom in point cloud analysis predominantly explores 3D geometries. It is often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these methods contain a significant number of learnable parameters, resulting in substantial computational costs and imposing memory burdens on CPU/GPU. Moreover, they are primarily tailored for object-level point cloud classification and segmentation tasks, with limited extensions to crucial scene-level applications, such as autonomous driving. To this end, we introduce PointeNet, an efficient network designed specifically for point cloud analysis. PointeNet distinguishes itself with its lightweight architecture, low training cost, and plug-and-play capability, while also effectively capturing representative features. The network consists of a Multivariate Geometric Encoding (MGE) module and an optional Distance-aware Semantic Enhancement (DSE) module. MGE employs operations of sampling, grouping, pooling, and multivariate geometric aggregation to lightweightly capture and adaptively aggregate multivariate geometric features, providing a comprehensive depiction of 3D geometries. DSE, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks, achieving notable performance improvements at a minimal cost. Extensive experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNetPart, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis. Notably, PointeNet outperforms PointMLP with significantly fewer parameters on ModelNet40, ScanObjectNN, and ShapeNetPart, and achieves a substantial improvement of over 2% in 3DAPR40 for PointRCNN on KITTI with a minimal parameter cost of 1.4 million. Code is publicly available at https://github.com/lipeng-gu/PointeNet.