PCB-YOLOV8X: a network for detecting micro-sized defects on PCB surfaces based on enhanced feature information.

With the growing popularity of autonomous vehicles, ensuring their safety has become a significant concern. Vehicle manufacturers have incorporated the Android operating system to enhance user convenience. However, the Android operating system's diversity and vulnerability may significantly impact autonomous vehicles because these natures lead to a critical threat in autonomous vehicle security: Android malware. The complex nature of multi‐data sources fusion in autonomous driving systems has resulted in low detection efficiency and accuracy for Android malware. Given the security requirements for autonomous‐driving systems, this article presents an Android malware classification method centered around the multifeature fusion and deep learning architecture designed to detect and classify Android malware within autonomous‐driving systems. The proposed MUDROID framework relies on the Android permission mechanism and behavioral feature to formulate a malware classification model. Comprising four essential components—feature extraction, representation, multifeature fusion, and classifier training, MUDROID leverages the advantages of CNN and GCN combination model and is adept at extracting and interpreting multidimensional features, consequently constructing a multifeature fusion classifier to amplify the efficiency and accuracy of Android malware detection and classification. The experiment results support MUDROID's superior classification performance, with an impressive Android malware detection accuracy rate of 98.69% and family classification (95.42%). The proposed method can detect and classify Android malware effectively, thus elevating the security and robustness of the self‐driving system. Additionally, the approach in this article also addresses feature representation issues in detecting malware variants, facilitating the recognition of Android malware variants across diverse platforms.

Accurate detection of surface defects for steel is essential to improve surface quality and service life. Deep learning (DL) used in steel surface defect detection can solve the problems of low efficiency and poor accuracy of traditional manual detection. The classic YOLOv5 as a DL method is used to accomplish defect detection tasks without attention mechanisms, resulting in a loss of global information. Besides, it is difficult to complete complex network detection tasks with low-configuration hardware, especially for surface defects with complex defect types and variable defect sizes. To solve these issues, this paper introduces an improved global feature reuse and hardware-aware YOLOv5 by using BoTNet, RepGhost, and EfficientRep model (BGE-YOLOv5). The multi-head self-attention layer is used to obtain global information and only part of the convolutional layers is replaced to avoid the excessive computational cost. The RepGhost model is introduced to extract the remaining feature information for feature reuse. EfficientRep is used to replace the original structure to achieve hardware-aware and to balance the detection veracity and efficiency. The distance IOU is replaced by SCYLLA-IOU to accelerate the iteration and improve stability. The results of the framework on the surface defect database (NEU-DET) show that BGE-YOLOv5 achieves a mean average precision of 79.5%, which is 10.3% greater than the baseline. The proposed BGE-YOLOv5 has a better performance in steel surface defect detection.

Currently, the intelligent defect detection of massive grid transmission line inspection pictures using AI image recognition technology is an efficient and popular method. Usually, there are two technical routes for the construction of defect detection algorithm models: one is to use a lightweight network, which improves the efficiency, but it can generally only target a few types of defects and may reduce the detection accuracy; the other is to use a complex network model, which improves the accuracy, and can identify multiple types of defects at the same time, but it has a large computational volume and low efficiency. To maintain the model's high detection accuracy as well as its lightweight structure, this paper proposes a lightweight and efficient multi type defect detection method for transmission lines based on DCP-YOLOv8. The method employs deformable convolution (C2f_DCNv3) to enhance the defect feature extraction capability, and designs a re-parameterized cross phase feature fusion structure (RCSP) to optimize and fuse high-level semantic features with low level spatial features, thus improving the capability of the model to recognize defects at different scales while significantly reducing the model parameters; additionally, it combines the dynamic detection head and deformable convolutional v3's detection head (DCNv3-Dyhead) to enhance the feature expression capability and the utilization of contextual information to further improve the detection accuracy. Experimental results show that on a dataset containing 20 real transmission line defects, the method increases the average accuracy (mAP@0.5) to 72.2%, an increase of 4.3%, compared with the lightest baseline YOLOv8n model; the number of model parameters is only 2.8 M, a reduction of 9.15%, and the number of processed frames per second (FPS) reaches 103, which meets the real time detection demand. In the scenario of multi type defect detection, it effectively balances detection accuracy and performance with quantitative generalizability.

In the context of resource constrained embedded terminals, the current deep learning-based technology for detecting surface defects in strip steel faces challenges with low detection accuracy and efficiency. This study introduces a lightweight convolution neural network (CNN) model for detecting surface defects of strip steel, which is based on deep learning. Through the integration of non-parameter attention mechanism SimAM, inverse residual structure, and depth-wise convolution module, a lightweight attention mobile backbone network is developed to achieve optimal feature extraction. To address the challenge of detecting small surface defects the context augmentation module is introduced to provide more information for small defects detection by using multi-scale features. To improve the efficiency of feature fusion and reduce parameter redundancy, the GVFPN neck network is proposed. The network aims to represent and deal with multi-scale features objectively while minimizing costs. The experimental results indicate that on the NEU-DET dataset, the proposed network attains an mAP of 78.8% and FPS at 97 frame/s. Moreover, the model requires only 2.39 M parameters and 3.1 G FLOPs. Compared to YOLOv5s, the proposed network reduces parameters by 65.9% and FLOPs by 80.7%, while achieving a 1.3% higher mAP and a 30 frame/s increase in detection speed. These results effectively demonstrate the effectiveness and superiority of the proposed method.

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.

ABSTRACTAt present, ultrasonic guided wave nondestructive testing technology is widely used in the detection of bolt anchorage defects. Traditional detection methods are confronted with problems such as serious signal noise interference, low detection accuracy, and poor real‐time performance. In this paper, a new model named HO‐VMD‐CNN‐BiLSTM is proposed to optimize the accuracy of signal decomposition and quality classification. The model accurately identifies internal defects in bolts through precise signal decomposition and feature extraction, achieving a high classification accuracy of 96.78% even in a complex noise environment. The model incorporates the Logistic‐Tent chaotic mapping optimization algorithm, which enhances global search capability, improves feature extraction, and increases detection efficiency and accuracy. The HO‐VMD‐CNN‐BiLSTM model offers an innovative and efficient solution for nondestructive testing of the bolt anchorage quality, enabling high‐precision structural assessments while addressing the issues of signal noise interference and feature extraction in traditional inspection methods. By overcoming challenges related to signal noise interference and feature extraction, the model provides technical support for real‐time monitoring and intelligent assessment of bolt anchorage quality.

In response to the low detection efficiency and accuracy of traditional manual methods for detecting road underground defects, this paper proposes an intelligent detection method based on ground penetrating radar (GPR). This method integrates the detection, classification, and localization of road underground defects. The approach uses Swin Transformer as a feature extraction network and utilizes the YOLOX object detection algorithm as a road underground defect detection model. It enables the detection of defect regions in three types of defect images: voids, non-compact areas, and underground pipelines. In addition, the collected radar signals are processed by Fourier transformation to obtain time-domain spectra and frequency-domain spectra, which are further analyzed to generate signal classification data set to achieve the defect classification. Finally, based on the relative positional relationship between the detected defect images and the GPS information collected by the GPR, the real positions of the defects on the map are automatically determined using the APIs provided by Amap (AutoNavi map). Experimental results show that this method achieves a precision and recall rate of 94.2% and 99.1%, respectively, for the detection of road underground defects, with an average precision of 94% and an average classification accuracy of 90%. The method significantly improves the accuracy and speed of road underground defect detection while meeting engineering requirements, making it highly valuable for practical road underground defect detection tasks.

The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and there is also a potential risk of secondary foreign body intrusion. A deep-learning-based intelligent detection method is proposed to solve the problem of low accuracy and low efficiency of small-scale FOD detection. Firstly, a dual light camera system is utilized for the collection of FOD data. It generates a dual light FOD dataset containing both infrared and visible light images. Subsequently, a multi-attention mechanism and a bidirectional feature pyramid are integrated into the baseline network YOLOv5. This integration prioritizes the extraction of foreign object features and boosts the network’s ability to distinguish FOD from complex backgrounds. Additionally, it enhances the fusion of higher-level features to improve the representation of multi-scale objects. To ensure fast and accurate localization and recognition of targets, the Complete-IoU (CIoU) loss function is used to optimize the bounding boxes’ positions. The experimental results indicate that the proposed model achieves a detection speed of 36.3 frame/s, satisfying real-time detection requirements. The model also attains an average accuracy of 91.1%, which is 7.4% higher than the baseline network. Consequently, this paper verifies the effectiveness and practical utility of our algorithm for the detection of small-scale FOD targets.

Sieve mesh defect detection is a critical yet challenging task in industrial quality control. Traditional manual inspection methods suffer from low efficiency and limited accuracy due to reliance on human vision. To overcome these limitations, this paper proposes an automated defect detection system for high-precision 325-mesh sieves based on machine vision. The system utilizes a high-resolution line-scan camera to acquire detailed images of the sieve mesh. A defect detection method based on an improved YOLOv4 architecture is then developed. Specifically, the original CSPDarknet53 backbone is replaced with an enhanced VGG-16 network to reduce computational complexity. Additionally, a multiscale spatial pyramid pooling (SPP) module is introduced to improve the detection of defects with varying sizes. The proposed method is evaluated on a self-constructed sieve defect dataset. Experimental results demonstrate that the system significantly improves both the accuracy and efficiency of defect detection, making it well-suited for practical applications in automatic sieve inspection.

The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods.

With the rapid development of the Internet, many things of people can be done online, and the Internet has been integrated into people's daily life. Web applications widely used in all walks of life are convenient for people to use, but also bring some security problems. Cross-Site Scripting(XSS) vulnerability detection method based on dynamic analysis is an effective XSS vulnerability detection method, which simulates real attack behavior to conduct penetration test on Web applications. However, there are still some problems such as low detection coverage, low detection efficiency and low detection accuracy. Based on this, this paper expounds the research status and background of XSS attack detection, puts forward the necessity of using machine learning algorithm for XSS attack detection, applies SVM algorithm to XSS detection, analyzes and extracts the features of XSS samples, selects radial basis kernel function for nonlinear SVM training, and verifies the generated detection model. It is found that the SVM model in this paper is also superior to the comparison algorithm in accuracy and false alarm rate.

During the production of steel, in view of the manufacturing engineering, transportation, and other factors, a steel surface may produce some defects, which will endanger the service life and performance of the steel. Therefore, the detection of defects on a steel surface is one of the indispensable links in production. The traditional defect detection methods have trouble in meeting the requirements of high detection accuracy and detection efficiency. Therefore, we propose the WFRE-YOLOv8s, based on YOLOv8s, for detecting steel surface defects. Firstly, we change the loss function to WIoU to address quality imbalances between data. Secondly, we newly designed the CFN in the backbone to replace C2f to reduce the number of parameters and FLOPs of the network. Thirdly, we utilized RFN to complete a new neck RFN to reduce the computational overhead and, at the same time, to fuse different scale features well. Finally, we incorporate the EMA attention module into the backbone to enhance the extraction of valuable features and improve the detection accuracy of the model. Extensive experiments are carried out on the NEU-DET to prove the validity of the designed module and model. The mAP0.5 of our proposed model reaches 79.4%, which is 4.7% higher than that of YOLOv8s.

At present, the detection of surface defects of paper-plastic composite bags still mainly depends on manual visual inspection, which is very inefficient and easy to cause false detection. Based on the algorithm which combines edge detection and adaptive region growth, binary images of the surface defect areas is extracted, six types of shape features and seven types of invariant moment features are defined by calculation as the basis to complete the effective extraction of the surface features, then a paper-plastic composite bags surface defect detection platform is built to complete the defect detection experiment, the defect screening experiment was completed by the rapid screening method of its surface defect image based on gradient projection difference. At the same time, based on Genetic Algorithm (GA), Particle Swarm Optimization, and Grey Wolf Optimization, parameter optimization models of Support Vector Machine were established, respectively. Through experimental comparison, some results show that the defect detection and classification accuracy rate reach 96.83% based on the GA, and the detection speed with the screening method is 3.75 times faster than that without the screening method. Therefore, the reliability of the image classification and screening method for bags surface defects proposed in this study is verified.

BackgroundAcoustic telemetry is an increasingly common method used to address ecological questions about the movement, behaviour, and survival of freshwater and marine organisms. The variable performance of acoustic telemetry equipment and ability of receivers to detect signals from transmitters have been well studied in marine and coral reef environments to inform study design and improve data interpretation. Despite the growing use of acoustic telemetry in large, deep, freshwater systems, detection efficiency and range, particularly in relation to environmental variation, are poorly understood. We used an array of 90 69-kHz acoustic receivers and 8 sentinel range transmitters of varying power output deployed at different depths and locations approximately 100–9500 m apart for 215 days to evaluate how the detection efficiency of acoustic receivers varied spatially and temporally in relation to environmental conditions.ResultsThe maximum distance that tags were detected ranged from 5.9 to 9.3 km. Shallow tags consistently had lower detection efficiency than deep tags of the same power output and detection efficiency declined through the winter months (December–February) of the study. In addition to the distance between tag and receiver, thermocline strength, surface water velocity, ice thickness, water temperature, depth range between tag and receiver, and number of fish detections contributed to explaining variation in detection efficiency throughout the study period. Furthermore, the most significant models incorporated interactions between several environmental variables and tag–receiver distance, demonstrating the complex temporal and spatial relationships that exist in heterogeneous environments.ConclusionsRelying on individual environmental variables in isolation to interpret receiver performance, and thus animal behaviour, may be erroneous when detection efficiency varies across distances, depths, or tag types. As acoustic telemetry becomes more widely used to study ecology and inform management, it is crucial to understand its limitations in heterogeneous environments, such as freshwater lakes, to improve the quality and interpretation of data. We recommend that in situ range testing and retrospective analysis of detection efficiency be incorporated into study design for telemetry projects. Furthermore, we caution against oversimplifying the dynamic relationship between detection efficiency and environmental conditions for the sake of producing a correction that can be applied directly to detection data of tagged animals when the intended correction may not be justified.

For the detection of wood surface defects, a convolutional neural network has a low detection efficiency and insufficient generalization ability, so it does not meet the requirements of online detection. Aiming to solve the above problems, the YOLOv3 baseline model, which has the advantage of multi-objective dynamic detection, was improved and applied to the online detection of wood surface defects. To solve the problem of the poor generalization ability of the network, GridMask was used to enhance the data and improve the robustness of the network. In order to solve the problem of the considerable amount of network parameter calculations and insufficient real-time performance, the residual block of the backbone network was changed to a Ghost block structure to achieve a lightweight model. Finally, the confidence loss function of the network was improved to reduce the influence of simple samples and negative samples on model convergence. The experimental results showed that, compared with the original network, the improved algorithm increased the mean average precision by 5.73% and the detection speed was increased to 28 frames per second (an increase of 11), which met the requirements for real-time industrial detection.