Fault Detection In Networks Research Articles

A bilateral feature fusion network for defect detection on mobile cameras

Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing.

Pyramid cross attention network for pixel-wise surface defect detection

Surface defect detection plays a crucial role in improving the overall quality of industrial production processes and ensuring that the resulting products meet the required quality standards. However, detecting these defects can be challenging due to various issues, including low image contrast, significant background noise, variable defect scales, and blurred boundaries. Although several segmentation networks have been developed to address these challenges, they still face difficulties in preserving fine-grained details during the encoding process and maintaining global features during the decoding process. Moreover, the simple skip connection that combines global and local information for feature fusion fails to consider their discrepancies and varying levels of significance. To overcome these limitations, this paper proposes a Pyramid Cross Attention Network (PCANet) for pixel-level surface defect detection. The encoder extracts multiresolution features, and the pyramid adaptive selection module (PASM) is introduced to supplement lost information and adaptively select information based on its importance. Furthermore, the cross attention fusion module (CAFM) is designed to address the incompatibility between the features extracted by the encoder and decoder in the raw skip connection, while strengthening defect areas and suppressing irrelevant noise using cross attention mechanisms. Finally, extensive experimental results demonstrate that the proposed PCANet outperforms other state-of-the-art methods in terms of mean Intersection over Union (mIoU). Specifically, it achieves an mIoU of 84.57 % in NEU-Seg and 79.37 % in MT_defect, respectively.

LHA-Net: A Lightweight and High-accuracy Network for Road Surface Defect Detection

LHA-Net: A Lightweight and High-accuracy Network for Road Surface Defect Detection

YOLO algorithm with hybrid attention feature pyramid network for solder joint defect detection

YOLO algorithm with hybrid attention feature pyramid network for solder joint defect detection

A Cascaded Network for Surface Defect Detection on Lead Frames in Production Lines

A Cascaded Network for Surface Defect Detection on Lead Frames in Production Lines

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

SRPCNet: Self-Reinforcing Perception Coordination Network for Seamless Steel Pipes Internal Surface Defect Detection

SRPCNet: Self-Reinforcing Perception Coordination Network for Seamless Steel Pipes Internal Surface Defect Detection

RDNet-KD: Recursive Encoder, Bimodal Screening Fusion, and Knowledge Distillation Network for Rail Defect Detection

RDNet-KD: Recursive Encoder, Bimodal Screening Fusion, and Knowledge Distillation Network for Rail Defect Detection

Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation

Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation

DSA-Net: An Attention-Guided Network for Real-Time Defect Detection of Transmission Line Dampers Applied to UAV Inspections

DSA-Net: An Attention-Guided Network for Real-Time Defect Detection of Transmission Line Dampers Applied to UAV Inspections

STMA-Net: A Spatial Transformation-Based Multiscale Attention Network for Complex Defect Detection With X-Ray Images

STMA-Net: A Spatial Transformation-Based Multiscale Attention Network for Complex Defect Detection With X-Ray Images

Reliable and Lightweight Adaptive Convolution Network for PCB Surface Defect Detection

Reliable and Lightweight Adaptive Convolution Network for PCB Surface Defect Detection

Optimizing SNARK networks via double metric dimension

<p>Doubly resolving sets (DRSs) provide a promising approach for source detection. They consist of minimal subsets of nodes with the smallest cardinality, referred to as the double metric dimension (DMD), that can uniquely identify the location of any other node within the network. Utilizing DRSs can improve the accuracy and efficiency of the identification of the origin of a diffusion process. This ability is crucial for early intervention and control in scenarios such as epidemic outbreaks, misinformation spreading in social media, and fault detection in communication networks. In this study, we computed the DMD of flower snarks $ J_{m} $ and quasi-flower snarks $ G_{m} $ by describing their minimal doubly resolving sets (MDRSs). We deduce that the DMD for the flower snarks $ J_{m} $ is finite and depends on the network's order, and the DMD for the quasi-flower snarks $ G_{m} $ is finite and independent of the network's order. Furthermore, our findings offer valuable insights into the structural features of complex networks. This knowledge can offer direction for future studies in network theory and its practical implementations.</p>

Incipient Fault Detection in Power Distribution Networks: Review, Analysis, Challenges, and Future Directions

Incipient Fault Detection in Power Distribution Networks: Review, Analysis, Challenges, and Future Directions

Enhanced fault localization in multi-terminal transmission lines using novel machine learning

Accurate fault location on transmission lines is paramount for ensuring the reliable and efficient operation of the electricity grid, which underpins every aspect of modern society. Existing fault localization methods for transmission lines often face shortcomings, particularly in scenarios involving multi-terminal transmission lines, where complexities arise due to dispersed generations and intricate network configurations. Traditional approaches may struggle to provide accurate fault localization, impacting the reliability and efficiency of the electricity grid. Research provide a unique fault localization technique in this article depends on Phasor Measurement Units (PMUs) and Bidirectional Gradient Boost Random Forest (BDGB-RF) machine learning technique to address these challenges. The proposed method offers several advantages over traditional methods, including enhanced accuracy and efficiency. By leveraging PMU data and BDGB-RF, the method provides a two-phase fault localization strategy, incorporating fault line selection based on nodal current imbalance and subsequent fault distance determination. Simulation results demonstrate the effectiveness of the proposed approach, achieving up to 97% accuracy in the majority of studied scenarios, even under different tapping configurations. The adoption of this approach could significantly impact the practices of experts in the field, facilitating more reliable fault detection and localization in complex transmission line networks. This, in turn, can contribute to the resilience and stability of power systems, ultimately improving grid reliability and minimizing downtime.

A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing

A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing

A Dynamic Weights-Based Wavelet Attention Neural Network for Defect Detection.

Automatic defect detection plays an important role in industrial production. Deep learning-based defect detection methods have achieved promising results. However, there are still two challenges in the current defect detection methods: 1) high-precision detection of weak defects is limited and 2) it is difficult for current defect detection methods to achieve satisfactory results dealing with strong background noise. This article proposes a dynamic weights-based wavelet attention neural network (DWWA-Net) to address these issues, which can enhance the feature representation of defects and simultaneously denoise the image, thereby improving the detection accuracy of weak defects and defects under strong background noise. First, wavelet neural networks and dynamic wavelet convolution networks (DWCNets) are presented, which can effectively filter background noise and improve model convergence. Second, a multiview attention module is designed, which can direct the network attention toward potential targets, thereby guaranteeing the accuracy for detecting weak defects. Finally, a feature feedback module is proposed, which can enhance the feature information of defects to further improve the weak defect detection accuracy. The DWWA-Net can be used for defect detection in multiple industrial fields. Experiment results illustrate that the proposed method outperforms the state-of-the-art methods (mean precision: GC10-DET: 6.0%; NEU: 4.3%). The code is made in https://github.com/781458112/DWWA.

Transformer-Based Multiscale Reconstruction Network for Defect Detection of Infrared Images

Transformer-Based Multiscale Reconstruction Network for Defect Detection of Infrared Images

A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications

A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications

Deep Learning Based Feature Discriminability Boosted Concurrent Metal Surface Defect Detection System Using YOLOv-5s-FRN

Computer vision and deep learning techniques are the most emerging technologies in this era. Both of these can greatly raise the rate at which defects on metal surfaces are identified while performing industrial quality checks. The identification of faults over metal surfaces can be viewed as a significant challenge since they are easily impacted by ambient factors including illumination and light reflections. This paper proposes novel metal surface defect detection network called as YOLOv-5s-FRN in response to the problems of ineffective detection brought by the conventional manual inspection system. The proposed system is developed through the integration of a novel architectural module called as Feature Recalibration Network (FRN) to the You Only Look Once-version-5 small network )YOLOv-5s(. In order to extract the global feature information from the provided image, FRN is able to evaluate the interdependencies between the channels. This improves the feature discrimination capability and prediction accuracy of the defect detection system. The incorporation of FRN structure makes YOLOv-5s architecture to selectively enhance the necessary features and discard the unwanted ones. Therefore, the proposed novel method will efficiently detect and classify the metal surface defects such as crazing, patches, inclusions, scratches, pitted surfaces and rolled in scale. North Eastern University surface defect database (NEU-DET) has been used to train and test the proposed architectural model. The suggested system has been compared with alternative models based on several performance matrices such as precision, recall and Mean Average Precision (mAP). It is observed that the proposed YOLOv-5s-FRN architecture provides significant performance improvement than state-of-the-art methods. The proposed system has been provided satisfactory results by means of improvement in mAP and time consumption. The proposed model has delivered value of mAP_0.5 as 98.05% and that of mAP_0.5:0.95 as 89.03%.