Defect Detection Model Research Articles

SteelGuard-yolo: Steel Surface Defect Detection Network Based on Improved YOLOv5s

Steel is playing an increasingly important role in industry, and the detection of defects on its surface is also of great significance. The complex and diverse defects on the steel surface bring great challenges to the detection. In this paper, we propose a SteelGuard-yolo based steel surface defect detection model, whose main role is to improve the existing algorithms for detecting steel surface defects. First, we design the C2f module with weight aggregation and introduce the BiFormer attention mechanism to improve the feature extraction capability of the model. Second, we design a new up-sampling structure and introduce the Multi-Scale Dilated Attention (MSDA) module to effectively improve the feature fusion capability of the model. Finally, we introduce the Simam attention mechanism and use EIoU as a new loss function to improve the robustness and accuracy of the model.SteelGuard-yolo has a powerful multi-scale feature fusion capability and achieves an ideal balance between latency and accuracy. The algorithm proposed in this paper is tested on the NEU-DET dataset and achieves an average accuracy of 69.0%, which compares favorably with most one- and two-stage detection algorithms.

Deep Learning-Based Multi-Species Appearance Defect Detection Model for MLCC

Deep Learning-Based Multi-Species Appearance Defect Detection Model for MLCC

YOLO’s multiple-strategy PCB defect detection model

YOLO’s multiple-strategy PCB defect detection model

Investigation on road underground defect classification and localization based on ground penetrating radar and Swin transformer

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.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

The Use of AI Technology in Digital Ceramic Technology

Abstract With the development of AI technology, the development of the ceramic industry has ushered in a good opportunity. This paper discusses the application of AI technology in the field of ceramics on the basis of deep convolutional generative adversarial network, using Adam optimizer to further improve the performance of the model, using the hidden space to add noise to each layer of the network, and proposing a digital ceramic texture image generation model based on DCGAN. Meanwhile, the K-Means algorithm and feature fusion mechanism with BWP as the evaluation index are introduced to improve the Faster R-CNN algorithm and construct the ceramic defect detection model. The scores of each subjective evaluation index of image generation quality are greater than 4, which are located in the interval of higher evaluation. The scores of IS, BRISQUE, and NIQE are 3.39, 22.57, and 4.43, and the quality of ceramic texture image generation of this paper’s model is better than that of other algorithms. The ceramic defect detection model’s detection accuracy for all five ceramic defects is higher than 92.68%, with an 8.54% improvement over the original algorithm’s defect detection performance. The research in this paper has important theoretical and practical significance for ceramic product design, quality control, and troubleshooting of production equipment.

Research on rapid detection of cross-scale defects in surface based on deep learning

The complex and diverse forms of surface defects in metal cutting, as well as their large scale span, present new challenges for deep learning algorithms. In addition, the existing defect detection models are generally characterized by high computational amount and complex structures, which is contrary to the high real-time performance and limited computing resources required in industrial applications. Based on this, this paper proposes a rapid detection method for cross-scale defects in surfaces based on deep learning. Firstly, a dataset of defect surfaces is collected and constructed through cutting experiments. Then, the experimental analysis reveals the insufficiency of the You Only Look Once version-5 s (YOLOv5s) network model for the detection of cross-scale defects on the surface. As a result, a RepVGG-Coordinate Attention-YOLOv5s (Rep-CA-YOLOv5s) network model, suitable for cross-scale defect detection, is proposed. This model optimizes the YOLOv5s network model from three perspectives, enhancing its ability to extract and fuse features for cross-scale defects. Finally, this paper investigates methods to improve detection speed while ensuring model accuracy. Two optimal Rep-CA-YOLOv5s sparse models are obtained through sparse training based on the γ scaling factor of the Batch Normalization (BN) layer and the filter weight, respectively. The relationship between detection accuracy, parameter quantity, and inference speed of these two sparse models under different pruning rates is explored. Experimental results indicate that the filter pruning method significantly improves model inference speed. At a 50 % pruning rate, optimal detection results can be achieved. Compared with the unpruning model, the pruned model reduced the inference speed by 55.67 %, while the mean Average Precision (mAP) only decreased by 0.1 %.

Multi-feature vision transformer for automatic defect detection and quantification in composites using thermography

This paper proposes a novel multi-feature vision transformer model for automatic defect detection and quantification in composites using thermography. Firstly, a sampling strategy for infrared data is proposed to address the issues of data class overlap and imbalance. Secondly, multiple feature fusion is employed to aggregate various important infrared features that exhibit strong correlations with defects. Furthermore, a multi-feature vision transformer (VIT) model that leverages self-attention mechanisms to capture global context and long-range dependencies in the composite images is developed. Experiments are performed to verify the effectiveness of the proposed method. The results show that the proposed method has excellent performance in terms of both accuracy and generalization capability.

A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy

Targeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery electrode defect detection model based on YOLOv8. Firstly, the lightweight GhostCony is used to replace the standard convolution, and the GhostC2f module is designed to replace part of the C2f, which reduces model computation and improves feature expression performance. Then, the coordinate attention (CA) module is incorporated into the neck network, amplifying the feature extraction efficiency of the improved model. Finally, the EIoU loss function is employed to swap out the initial YOLOv8 loss function, which improves the regression performance of the network. The empirical findings demonstrate that the enhanced model exhibits increments in crucial performance metrics relative to the original model: the precision rate is elevated by 2.4%, the recall rate by 2.3%, and the mean average precision (mAP) by 1.4%. The enhanced model demonstrates a marked enhancement in the frames per second (FPS) detection rate, significantly outperforming other comparative models. This evidence indicates that the enhanced model aligns well with the requirements of industrial development, demonstrating substantial practical value in industrial applications.

Unsupervised Wafer Defect Classification Model Based On Joint Reconstruction And Clustering

Defect detection of unpatterned wafer is very important for determining the causes of wafer defects, and it is also a significant way to improve production yield. At present, the defect detection model based on the deep learning method has been widely used and has shown promising performance. However, the labor cost of supervised learning method based on labeled samples is very expensive, so the wafer defect detection model based on unsupervised learning is a future research direction. In this paper, we propose an unsupervised wafer defect classification model(UWDDM), in which the reconstruction loss is used to train the feature extractor of wafer defects, and the classifier is trained based on the cross entropy loss of clustering results and classification results. The experimental results on the real dataset MixedWM38 show that the proposed model has higher recognition accuracy and less inference time than other unsupervised models. This unsupervised learning method has great potential for wafer defect detection. It can avoid the expensive manual labeling cost and provide an effective way to automatically detect wafer defects.

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies.

Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.

Metal Surface Defect Detection Based on Metal-YOLOX

Article Metal Surface Defect Detection Based on Metal-YOLOX Xiaoli Yue , Jiandong Chen , and Guoqiang Zhong * 1 College of Computer Science and Technology, Ocean University of China, Qingdao 266000, China * Correspondence: gqzhong@ouc.edu.cn Received: 14 June 2023 Accepted: 13 September 2023 Published: 21 December 2023 Abstract: Due to the limitations of manufacture technologies, working environments and other conditions, metals (such as steel and aluminum) are susceptible to surface defects during the production process. Therefore, defect detection is an indispensable part of metal manufacturing. This paper innovatively proposes a one-stage defect detection model named Metal-YOLOX. Metal-YOLOX addresses the limitations in existing models posed by large variances in defect features and inadequate balance between detection accuracy and efficiency. Firstly, the composite convolution module of Metal-YOLOX integrates texture, dilated and deformable convolutions to filter out irrelevant features and extract effective feature information. Secondly, the feature cross-fusion module (HCNet) alleviates the problem of large dimensional differences in defects. HCNet uses skip connections to establish the connection between the original multi-scale features and the output nodes, and reduces the addition of redundant information. Thirdly, Metal-YOLOX adopts the deep separable convolution and global channel reduction. This lightweight design helps reduce computational complexity. Finally, detailed experiments demonstrate that, in terms of mean average precision, Metal-YOLOX achieves 79.83, 69.14, and 81.22 on the NEU-DET, GC-10 and Aluminum datasets, respectively. Furthermore, Metal-YOLOX dramatically reduces parameter number and computational complexity. The experiments validate that the Metal-YOLOX model improves the detection performance, maintains the detection speed, and meets the real-time requirements.

AN APPLICATION OF A MATERIAL DEFECT DETECTION SYSTEM USING ARTIFICIAL INTELLIGENCE

In recent years, detecting defects in materials has become an important element in many industries, such as automotive, construction, textile manufacturing, and many others. Material defects, such as stains, cracks, dents, and others, can affect the quality and durability of the product, which can have serious consequences for safety, performance, and product quality. Therefore, it is important to detect and remove material defects as quickly and accurately as possible. Traditional methods of detecting material defects, such as manual inspections or simple visual algorithms, can be unreliable and, above all, time-consuming. In recent years, however, new tools based on intelligent algorithms (Liu et al., 2023) or structures (Neethu et al., 2015), such as neural networks, have emerged that enable automation and significantly increase the precision of defect detection. One of the most popular tools for object detection is the YOLOv5 (Redmon et al., 2016) (You Only Look Once version 5) model, which is based on neural networks and designed for fast and accurate object detection in images. The YOLOv5 model can be trained to detect specific types of material defects, such as stains, cracks, dents, etc. To use the YOLOv5 model for defect detection in materials, images of the material with specified objects of interest are needed, which will be used as input data for the model training. The YOLOv5 model analyses the images and determines rectangular frames around each defect indicating its location, and then assigns a selected label (in this case, the name of the defect). As a result, the YOLOv5 model is able to detect defects in new material images, regardless of their location, colour, and size. As the work progresses, it is also possible to prepare new images and retrain the model for new defects or to improve the performance of existing ones. As a result of the research, the obtained results at a 95% level of recognized defects certainly qualify the applied technology for professional use after appropriate workstation preparation and a sufficiently large amount of data (preferably 1000 or more).

Retracted: A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems

Retracted: A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains. For this reason, a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper. First, a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples. Second, an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image, which improved the accuracy of defect detection. Subsequently, to optimize the detection performance of the proposed model, the Mish activation function was used to design the block module of the feature extraction network. Finally, the proposed rail defect detection model was trained. The experimental results showed that the precision rate and F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} score of the proposed method were as high as 98%, while the model’s recall rate reached 99%. Specifically, good detection results were achieved for different types of defects, which provides a reference for the engineering application of internal defect detection. Experimental results verified the effectiveness of the proposed method.

Defect segmentation with local embedding in industrial 3D point clouds based on transformer

Three-dimensional (3D) defect detection provides an effective method for improving industrial production efficiency. However, the 3D dataset is scarce, which is valuable for the industrial production field. This study proposes a new approach for detecting defect point clouds, which can provide an end-to-end 3D defect detection model. A self-attention mechanism is used to enrich the semantic relationships between local neighborhood features and global features based on the connection between them. Through adding multi-channel features, the rich structural features of the target point cloud are obtained, and the defect areas are accurately segmented to finally complete the 3D point cloud defect detection task. Furthermore, the multi-feature fusion in the model makes the segmented defect regions closer to the ground truth. Our method outperforms four state-of-the-art point cloud segmentation methods in terms of both segmentation region accuracy and defect detection point cloud accuracy. In the field of 3D defect detection, it provides an effective method to detect 3D information of industrial products.

Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process

In the manufacturing industry, which is facing the 4th Industrial Revolution, various process data are being collected from various sensors, and efforts are being made to construct more efficient processes using these data. Many studies have demonstrated high accuracy in predicting defect rates through image data collected during the process using two-dimensional (2D) convolutional neural network (CNN) algorithms, which are effective in image analysis. However, in an environment where numerous process data are recorded as numerical values, the application of 2D CNN algorithms is limited. Thus, to perform defect prediction through the application of a 2D CNN algorithm in a process wherein image data cannot be collected, this study attempted to develop a defect prediction technique that can visualize the data collected in numerical form. The polyurethane foam manufacturing process was selected as a case study to verify the proposed method, which confirmed that the defect rate could be predicted with an average accuracy of 97.32%. Consequently, highly accurate defect rate prediction and verification of the basis of judgment can be facilitated in environments wherein image data cannot be collected, rendering the proposed technique applicable to processes other than those in this case study.

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.

Computer vision defect detection on unseen backgrounds for manufacturing inspection

Visual defect detection is an important aspect of quality inspection. High performance manufacturing can benefit from automating defect detection, and deep learning techniques are the current state of the art for this computer vision task. The premise of deep learning is that an over parameterized model learns to generalize in performing a task such as classification or object detection by exposure to a wide variety of training examples. In the canonical example, a classifier that has seen thousands of pictures of dogs and cats in different situations and backgrounds will be able to generalize to tell one animal from the other in a newly obtained photo. The assumption that the training data contains great variety is not met in typical manufacturing data sets. Data is highly repetitive and mostly represents defect-free products, meaning there are few images of defects or deviations to learn from. In this training regime, deep learning models are easily over-fit to the training data and can fail to detect defects in the face of variations such as position, lighting, or data drift. In this work, we explore training defect detection models with images of defects on different backgrounds and in different locations, in order to approximate the exposure to highly diverse data sets that is an assumption of a well-trained deep learning model. We demonstrate how models trained on diverse images containing a common defect type can pick defects out in new circumstances. Such generic models could be more robust to new defects not found in data collected for training, and can reduce data collection impediments to implementing visual inspection on production lines. Additionally, we demonstrate that object detection models trained to predict a label and bounding box outperform classifiers that predict a label only on held out test data typical of manufacturing inspection tasks. Finally, we studied the factors that affect generalization in order to train models that work under a wider range of conditions.

DGNet: An Adaptive Lightweight Defect Detection Model for New Energy Vehicle Battery Current Collector

DGNet: An Adaptive Lightweight Defect Detection Model for New Energy Vehicle Battery Current Collector