Identification Of Surface Defects Research Articles

Climbing robot-assisted quantitative identification of sheath surface defects of stay cables

Sheath surface defect identification of stay cables using climbing robots or drones is gaining popularity. However, effective quantitative identification approaches remain challenging. This study proposes a cable climbing robot assisted quantitative identification method to address sheath surface defects combining the deep learning method and image processing techniques. First, standard and high-resolution cable sheath images, collected by an improved cable climbing robot with exclusive scales indicating the defect size, were categorised into three datasets according to defect types. Subsequently, a mask region-based conventional neural network (Mask R-CNN) model was developed for defect capture, with a connected domain algorithm quantifying defect areas. Perspective projection was applied to the defect masks to reduce the image distortion effect. The test results show that the model can achieve over 89% precision, recall and F 1-score for three defect types, with masks presenting overlapping rates reach up to 87% compared to the ground-truth regions. The quantitative identification results are promising to facilitate stay cable assessment and maintenance. Future study will be focused on extending the volume and categories of the dataset and improving the performance of the model.

Lightweight-detection: The strip steel surface defect identification based on improved YOLOv5d

The surface defect of strip steel seriously affects the product quality, the current identification methods have the inherent defects of low detection efficiency and poor detection accuracy, and it is important an improved an algorithm based on the modified YOLOv5 framework to enhance the defect detection effectiveness and accuracy. To create a more lightweight network model, the C3 module and part of the convolutional structure in the original YOLOv5 network were replaced with the GhostBottleneck structure. Additionally, the Coordinate Attention (CA) mechanism was incorporated into the Backbone section to compensate for the original model's lack of attention mechanisms. To address the angular mismatch between the actual frame and the predicted bounding box, a limitation overlooked by the CIOU loss function was further refined. Experimental results demonstrate that the enhanced YOLOv5s-CSG model outperformed the original YOLOv5s by 7.3 %, achieving a significant 37.9 % reduction in model size and an mAP value of 77.5 %. Furthermore, the detection precision and speed were notably higher compared to several widely-used algorithms. The proposed YOLOv5s-CSG model is well-suited for deployment on mobile devices and shows great potential for rapidly and accurately detecting both the type and location of strip surface defects, surpassing the performance of existing methods.

Computer vision–based surface defect identification method for weld images

Welding is the main connection type used in steel structure bridges. As the weld quality significantly affects the performance of a structure, weld defect detection is crucial in the performance evaluation of bridges. With the continuous development of computer vision (CV) techniques and image acquisition equipment, CV-based methods are being increasingly applied to weld defect detection. However, accurate identification of welding defects from several images is challenging. This paper proposes a CV-based method for detecting and quantifying surface porosity defects in welds. CV techniques were used to identify and extract weld defects, and the pixel-level sizes of the weld defects were obtained. The actual sizes of the weld defects were calculated based on a conversion between the image size and actual size. The results show that the proposed image processing method has low complexity and high accuracy in detecting weld defects, providing accurate measurement of the defect size. It is a fast and non-contact detection method for weld defect detection.

Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7

Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection task, yet issues with missed detections, false alarms, and low confidence levels persist. Therefore, this study proposes an improved YOLOv7 algorithm to further enhance the performance and generalization of surface defect detection in AFP. Firstly, to enhance the model’s feature extraction capability, the BiFormer attention mechanism is introduced to make the model pay more attention to small target defects, thereby improving feature discriminability. Next, the AFPN structure is used to replace the PAFPN at the neck layer to strengthen feature fusion, preserve semantic information to a greater extent, and finely integrate multi-scale features. Finally, WIoU is adopted to replace CIoU as the bounding box regression loss function, making it more sensitive to small targets, enabling more accurate prediction of object bounding boxes, and enhancing the model’s detection accuracy and generalization capability. Through a series of ablation experiments, the improved YOLOv7 shows a 10.5% increase in mAP and a 14 FPS increase in frame rate, with a maximum detection speed of 35 m/min during the AFP process, meeting the requirements of online detection and thus being able to be applied to surface defect detection in AFP operations.

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Abstract The presence of surface defects in wire ropes (WR) may lead to potential safety hazards and performance degradation, necessitating timely detection and repair. Hence, this paper proposes a method for detecting surface defects in WR based on the deep learning models YOLOv8s and U-Net, aiming to identify surface defects in real-time and extract defect data, thereby enhancing the efficiency of surface defect detection. Firstly, the ECA attention mechanism is incorporated into the YOLOv8 algorithm to enhance detection performance, achieving real-time localization and identification of surface defects in WR. Secondly, in order to obtain detailed defect data, the U-Net semantic segmentation algorithm is employed for morphological segmentation of defects, thereby obtaining the contour features of surface defects. Finally, in conjunction with OpenCV technology, the segmentation results of the defects are quantified to extract data, obtaining parameters such as the area and perimeter of the surface defects in the WR. Experimental results demonstrate that the improved YOLOv8-ECA model exhibits good accuracy and robustness, with the model’s mAP@0.5 reaching 84.78%, an increase of 1.13% compared to the base model, an accuracy rate of 90.70%, and an FPS of 65. The U-Net model can efficiently perform segmentation processing on surface defects of WR, with an mIOU of 83.54% and an mPA of 90.78%. This method can rapidly, accurately, and specifically detect surface defects in WR, which is of significant importance in preventing industrial production safety accidents.

RSDNet: A New Multiscale Rail Surface Defect Detection Model.

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Magneto-acoustic Fusion Sensing for Surface and Subsurface Defect Differentiation in the Ferromagnetic Material

The conventional periodic permanent magnet (PPM) shear horizontal (SH0) guided wave electromagnetic acoustic transducer (EMAT) has a good detection capability for defects on both the surface and subsurface of the material. However, it is difficult to distinguish between surface and subsurface defects in the material using only the amplitude of the defect echo. In this paper, SH0 guided wave is excited using the SH0 guided wave EMAT with PPM configuration, which is used to achieve the detection and evaluation of defects on the surface and subsurface of ferromagnetic metal; on the other hand, the local magnetic field perturbation (MFP) effect of the PPM configuration on ferromagnetic material is fully exploited to achieve the identification of surface defects by picking up the MFP of the material at the defects. The proposed magneto-acoustic fusion effect sensor has an improved spatial magnetic field at both the mechanical construction and the magnetic sensor compared to the traditional magnetic flux leakage (MFL) detection method. Both simulation and experiment show that the proposed magneto- acoustic fusion effect sensing method based on the PPM configuration is capable of detecting the defects on the surface and subsurface of metal material, while the MFP effect in the fusion sensor can be used to help differentiate between the surface and subsurface defects in the material.

Surface defect identification method for hot-rolled steel plates based on random data balancing and lightweight convolutional neural network

Surface defect identification method for hot-rolled steel plates based on random data balancing and lightweight convolutional neural network

Identification and classification of surface defects for digital twin models of the workpiece.

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity. However, the digital twin model has the characteristics of high fidelity and scalability, and the digital twin surface can be obtained by a device with a scanning accuracy of 0.02mm to achieve the representation of the real surface of the workpiece. The surface defect detection system for digital twin models is proposed based on the improved YOLOv5 model in this paper. Firstly, the digital twin model of the workpiece is reconstructed by the point cloud data obtained by the scanning device, and the surface features with defects are captured. Subsequently, the training dataset is calibrated based on the defect surface, where the defect types include Inclusion, Perforation, pitting surface and Rolled-in scale. Finally, the improved YOLOv5 model with CBAM mechanism and BiFPN module was used to identify the surface defects of the digital twin model and compare it with the original YOLOv5 model and other common models. The results show that the improved YOLOv5 model can realize the identification and classification of surface defects. Compared with the original YOLOv5 model, the mAP value of the improved YOLOv5 model has increased by 0.2%, and the model has high precision. On the basis of the same data set, the improved YOLOv5 model has higher recognition accuracy than other models, improving 11.7%, 3.4%, 6.2%, 33.5%, respectively. As a result, this study provides a practical and systematic detection method for digital twin model surface during the intelligent production process, and realizes the rapid screening of the workpiece with defects.

GDCP-YOLO: Enhancing Steel Surface Defect Detection Using Lightweight Machine Learning Approach

Surface imperfections in steel materials potentially degrade quality and performance, thereby escalating the risk of accidents in engineering applications. Manual inspection, while traditional, is laborious and lacks consistency. However, recent advancements in machine learning and computer vision have paved the way for automated steel defect detection, yielding superior accuracy and efficiency. This paper introduces an innovative deep learning model, GDCP-YOLO, devised for multi-category steel defect detection. We enhance the reference YOLOv8n architecture by incorporating adaptive receptive fields via the DCNV2 module and channel attention in C2f. These integrations aim to concentrate on valuable features and minimize parameters. We incorporate the efficient Faster Block and employ Ghost convolutions to generate more feature maps with reduced computation. These modifications streamline feature extraction, curtail redundant information processing, and boost detection accuracy and speed. Comparative trials on the NEU-DET dataset underscore the state-of-the-art performance of GDCP-YOLO. Ablation studies and generalization experiments reveal consistent performance across a variety of defect types. The optimized lightweight architecture facilitates real-time automated inspection without sacrificing accuracy, offering invaluable insights to further deep learning techniques for surface defect identification across manufacturing sectors.

Cover Image

AbstractThe cover image is based on the Research Article Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network by Yang Wang et al., https://doi.org/10.1002/pc.27952.image

Improved Deep Learning Model for Workpieces of Rectangular Pipeline Surface Defect Detection

This study introduces a novel approach to address challenges in workpiece surface defect identification. It presents an enhanced Single Shot MultiBox Detector model, incorporating attention mechanisms and multi-feature fusion. The research methodology involves carefully curating a dataset from authentic on-site factory production, enabling the training of a model with robust real-world generalization. Leveraging the Single Shot MultiBox Detector model lead to improvements integrating channel and spatial attention mechanisms in the feature extraction network. Diverse feature extraction methods enhance the network’s focus on crucial information, improving its defect detection efficacy. The proposed model achieves a significant Mean Average Precision (mAP) improvement, reaching 99.98% precision, a substantial 3% advancement over existing methodologies. Notably, the proposed model exhibits a tendency for the values of the P-R curves in object detection for each category to approach 1, which allows a better balance between the requirements of real-time detection and precision. Within the threshold range of 0.2 to 1, the model maintains a stable level of precision, consistently remaining between 0.99 and 1. In addition, the average running speed is 2 fps lower compared to other models, and the reduction in detection speed after the model improvement is kept within 1%. The experimental results indicate that the model excels in pixel-level defect identification, which is crucial for precise defect localization. Empirical experiments validate the algorithm’s superior performance. This research represents a pivotal advancement in workpiece surface defect identification, combining technological innovation with practical efficacy.

Identification of surface defects on solar PV panels and wind turbine blades using attention based deep learning model

The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects. The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.

AnomalySeg: Deep Learning-Based Fast Anomaly Segmentation Approach for Surface Defect Detection

Product quality inspection is a crucial element of industrial manufacturing, yet flaws such as blemishes and stains frequently emerge after the product is completed. Most research has utilized detection models and avoided segmenting networks due to the unequal distribution of faulty information. To overcome this challenge, this work presents a rapid segmentation-based technique for surface defect detection. The proposed model is based on a modified U-Net, which introduces a hybrid residual module (SAFM), combining an improved spatial attention mechanism and a feedforward neural network in place of the remaining downsampling layers, except for the first layer of downsampling in the encoder, and applies this residual module to the decoder structure. Dilated convolutions are also incorporated in the decoder to obtain more spatial information about the feature defects and to reduce the gradient vanishing problem of the model. An improved hybrid loss function with Dice and focal loss is introduced to alleviate the small defect segmentation problem. Comparative experiments were conducted on different segmentation-based inspection methods, revealing that the Dice coefficient (DSC) evaluated by the proposed approach is better than previous generic segmentation benchmarks on KolektorSDD, KolektorSDD2, and RSDD datasets, with fewer parameters and FLOPs. Additionally, the detection network displays higher precision in recognizing the characteristics of minor flaws. This paper proposes a practical and effective technique for anomaly segmentation in surface defect identification, delivering considerable improvements over previous methods.

Withdrawal: Identification and classification of surface defects for digital twin models of the workpiece.

Withdrawal: Identification and classification of surface defects for digital twin models of the workpiece.

Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

As the critical dimensions of semiconductor manufacturing processes gradually decrease, the requirements for production yield management become increasingly stringent. During the manufacturing process, there are many different types of defects, such as micron-sized particles, millimeter-sized scratches, etc. Multiple categories and different scales bring great challenges to the detection and identification of defects. This paper provides a full-flow surface defect identification method based on spot scanning scattering for unpatterned wafers. First, an adaptive threshold method with dynamic kernel windows is used to perform line-by-line scanning inspection of the wafer Mercator image. The 3σ decision strategy is used to avoid the impact of defects on background estimation and to improve detection sensitivity. After morphological processing, connected domain analysis is performed to obtain the defect mask, and feature information such as the shape, size, and distribution of the defect is extracted. Finally, the defect identification is performed by rules based binning, and the identified defects are converted into wafer polar coordinate image for display and analysis. In the experiments, the proposed method is used to identify micron-scale particles as well as large scratches on the millimeter scale for SiC wafers. Relative to the actual production rate requirement of 20 wafers per hour, the analysis time for a 6-inch wafer is 24.4 s, which can meet the requirement. Meanwhile, the test results illustrate the effectiveness of the method. The proposed method is recommended for early-stage defect detection and identification of unpatterned wafers.

Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network

AbstractIn the aerospace sector, the identification and extraction of surface defects on composite parts is particularly important as they can have a very serious impact. This paper proposes a new extraction method for surface defects of composite materials, where 3D point clouds on the surface of composite workpieces are directly manipulated to finally extract a point cloud of defects containing both morphological and spatial location information. The specific operation explores the influencing factors in the formation and extraction process of composite surface defects and summarizes them into five categories: material type, fiber direction, curvature algorithm, neighborhood size, and filtering threshold. The Multilayer Perceptron‐Moth Flame algorithm two‐stage network model was constructed, which can reveal the relationship between these five types of influencing factors and the formation and extraction of surface defects in composites in the forward direction with an accuracy of 93.07%. It can also achieve optimal parameter recommendation in the reverse direction.Highlights Automatic identification and extraction of defects on the surface of composite workpieces is achieved based on 3D point cloud and curvature calculation. The forward direction reveals the relationship between the surface defect formation process and the material type and fabric direction. The reverse direction can automatically select the best curvature algorithm, neighborhood size, and filtering condition parameters. Build a Multilayer Perceptron‐Moth Flame Algorithm network structure based on multilayer perceptron and moth‐flame algorithm for model training of defect extraction, with an accuracy of 93.07%.

Defect detection of the surface of wind turbine blades combining attention mechanism

The proposed work introduces a novel, lightweight feature fusion network model based on the attention mechanism to address the issues of high time consumption and poor effectiveness in wind turbine blade surface defect detection. A bidirectional feature fusion network enhances the YOLOX network. The model employs an attention mechanism module to learn channel and spatial feature information. A classification loss function with an attenuation factor is designed to tackle the unbalanced distribution of learning weights for multiple samples in the blade surface defect detection task. Additionally, to mitigate recognition loss caused by target occlusion or overlap, the Soft-NMS method is utilized to eliminate redundant detection boxes. Sample imbalance is addressed by creating new samples using a geometric transformation-based multi-sample fusion data enhancement method and unsupervised learning MFF-GAN image editing techniques. A feature fusion network comparison test, classification loss function comparison test, and attention mechanism module position ablation test were completed using the blade surface defect dataset. The results indicate that the blade surface defect detection model incorporating the attention mechanism can effectively identify five types of defects with an average detection accuracy of mAP-0.5, achieving approximately 95.03 % and a detection frame rate of about 54.56 frame·s−1. Compared with the YOLOX-s network, YOLOv7-tiny network, and YOLOv8-s network, this model can improve the recognition rate and shorten the detection time, achieving high precision and rapid identification and localization of blade surface defects.

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Surface defects generated during the production process of steel balls can lead to bearing failures, which makes it crucial to promptly detect and classify these defects. Defects classify is helpful for analysis and improving the production process. An algorithm that incorporates K-fold cross-validation (K-CV) with improved grid search is proposed to optimize the parameters of SVM, in order to detect surface defects with steel balls. Principal Component Analysis (PCA) was employed to reduce the dimensionality of the effective features data. The K-CV algorithm was employed in conjunction with an improved grid search method to find the optimal parameters “c” and “g.” This approach not only reduced the search time but also diminished the influence of individual samples on the model, thereby enhancing its robustness and ultimately improving the classification accuracy. The model’s performance was evaluated using a confusion matrix, and a comparison was made with three other machine learning models. The experimental results demonstrated the effectiveness of the proposed algorithm in classifying defects on highly reflective metal surfaces such as steel balls. The model achieved a classification accuracy of 97.15%.

Citrus surface defect identification based on PCS-2D-Otsu and CGWO-DT-SVM

Citrus surface defect identification based on PCS-2D-Otsu and CGWO-DT-SVM