Metal Surface Defects Research Articles

Metal surface defect detection based on improved YOLOv5

During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed.

Metal Surface Defect Detection Method Based on Improved Cascade R-CNN

Abstract In contemporary industrial systems, ensuring the quality of object surfaces has become an essential and inescapable aspect of factory inspections. Cascade regional convolutional neural network (cascade R-CNN), an object detection and instance segmentation algorithm based on deep learning, has been widely applied in numerous industrial applications. Nonetheless, there is still space for improving the detection of defects on metal surfaces. The cascade R-CNN does not currently have good results in metal defect detection, but after improving it by combining some newly proposed modules, cascade R-CNN has a good performance. This article proposes an enhanced metal defect detection method based on cascade R-CNN. Specifically, the improved backbone network is employed to acquire the features of images, which enables more precise localization. Additionally, up and down sampling is combined to extract multiscale defect feature maps, and contrast histogram equalization enhancement is utilized to tackle the issue of unclear contrast in the data. Experimental results demonstrate that the proposed approach achieves a mean average precision (mAP) of 0.754 on the NEU-DET dataset and outperforms the cascade R-CNN model by 9.2%.

Metal Surface Defect Detection Based on a Transformer with Multi-Scale Mask Feature Fusion

In the production process of metal industrial products, the deficiencies and limitations of existing technologies and working conditions can have adverse effects on the quality of the final products, making surface defect detection particularly crucial. However, collecting a sufficient number of samples of defective products can be challenging. Therefore, treating surface defect detection as a semi-supervised problem is appropriate. In this paper, we propose a method based on a Transformer with pruned and merged multi-scale masked feature fusion. This method learns the semantic context from normal samples. We incorporate the Vision Transformer (ViT) into a generative adversarial network to jointly learn the generation in the high-dimensional image space and the inference in the latent space. We use an encoder–decoder neural network with long skip connections to capture information between shallow and deep layers. During training and testing, we design block masks of different scales to obtain rich semantic context information. Additionally, we introduce token merging (ToMe) into the ViT to improve the training speed of the model without affecting the training results. In this paper, we focus on the problems of rust, scratches, and other defects on the metal surface. We conduct various experiments on five metal industrial product datasets and the MVTec AD dataset to demonstrate the superiority of our method.

AP-Net: a metallic surface defect detection approach with lightweight adaptive attention and enhanced feature pyramid

AP-Net: a metallic surface defect detection approach with lightweight adaptive attention and enhanced feature pyramid

Residual shape adaptive dense-nested Unet: Redesign the long lateral skip connections for metal surface tiny defect inspection

The state-of-the-art metal surface defect inspection methods have two problems: (1) they are sensitive to tiny defects because of their extreme small sizes, and (2) they cannot accurately locate the random appeared defects whose semantic relationship with the background context is weak. To solve these problems, Residual Shape Adaptive Dense-nested Unet, a pixel-based defect inspection method is proposed, to obtain the exact shape and location of the defect, by (1) assembling different depth Unet branches with dense skip connections as the feature extractor to combine multi-semantic level visual features; (2) adding Residual Shape Adaptive modules on the dense skip connections to help the model locate the defect regions; and (3) introducing the multi-branch training method which enables model pruning to reduce redundant parameters and accelerate the inspection speed. Experiments are conducted and demonstrated that the Residual Shape Adaptive Dense-nested Unet achieves the best performance among the state-of-the-art defect inspection methods.

An Enhanced YOLOv5-Based Algorithm for Metal Surface Defect Detection

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability.

Adaptive-MAML: Few-shot metal surface defects diagnosis based on model-agnostic meta-learning

The rapid development of artificial intelligence has further increased the level of intelligence in the field of metal surface defect diagnosis. In general, metal surface defects are difficult to collect. Considering the problem of insufficient sample size of defects, we propose a framework for metal surface defect diagnosis: Adaptive-MAML, which consists of an improved MAML framework and neural network model. Adaptive-MAML proposes a meta-augmentation method (MetaAug) to automatically generate virtual samples during the training process to alleviate the defect sample shortage problem and overcome the overfitting problem. It also uses a hyperparametric adaptive strategy based on gradient descent (HASGD) to improve the stability and scalability of the training process. Experimental results on the FSC-20 and NEU-CLS-64 datasets show that the system exhibits better results in surface defect classification compared to other state-of-the-art methods. In addition, we further validate the generalization of the framework by applying it to the synthetic DAGM.

A novel multi-scale cross-patch attention with dilated convolution (MCPAD-UNET) for metallic surface defect detection

A novel multi-scale cross-patch attention with dilated convolution (MCPAD-UNET) for metallic surface defect detection

Contrastive self-supervised representation learning framework for metal surface defect detection

Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs.

ICA-Net: Industrial defect detection network based on convolutional attention guidance and aggregation of multiscale features

Detecting surface defects in the industry is essential for improving the quality of industrial products and maintaining product safety. However, problems such as the similarity of defects, significant variation in the scale of the target object, and the balance between detection speed and accuracy in industrial inspection scenarios have been considerable research topics in this field. This paper proposes an industrial defect detection network based on convolutional attention-guided and aggregated multiscale features to address these issues (ICA-Net). Firstly, for similarity defects in complex backgrounds, this paper proposes a backbone network with a combination of lightweight convolutional blocks and self-attentive modules to fully extract images’ local and global information and enhance the network’s expressiveness. Secondly, to make full use of the shallow fine-grained features and deep semantic features of the backbone network to improve the detection capability of defects with significant scale changes, this paper designs a cross-layer multiscale feature fusion network (CEF-Net), which fully fuses the features of adjacent layers and cross-layers through a reweighting feature strategy to enrich the network feature transfer path and ensure the efficient fusion of different scale features in the network. At the same time, the fine-grained feature fusion module (FFM) is used to fuse elements from multiple layers to extract more contextual information, enhance the extraction of fine-grained features and improve the detection capability of complex small targets. Finally, to address the problems of inaccurate regression localization and low detection accuracy of defects in existing industrial algorithms, a new IoU loss function (G-IOU) is proposed for regressing the intersection part of the predicted frame and the actual structure according to the aspect ratio of the real frame during the model regression to improve the accuracy and stability of detection. The experimental results show that 94.1%, 98.6%, 99.4%, 98.8% and 96.5% of mAP@.5 are obtained on steel, PCB, aluminium, automobile and Xsteel steel metal surface defect datasets, respectively, and 48 FPS is achieved, which is superior to the current mainstream detectors and meets the needs of practical industrial production.

Surface defects detection in metal materials repaired by laser surfacing of seal welds

Laser surfacing repair technology for sealing welds is widely used in metal repair. Due to welding technology and usage scenarios, process defects on the metal surface are inevitable. Therefore, ultrasonic surface wave technology is used to analyze the surface defects of metal materials. Principal Component Analysis (PCA) is used to extract the main defect signals on the metal surface, and synthetic aperture focusing technology is used to reduce imaging errors. Considering the lack of PCA in imaging defects, wavelet domain hidden Markov models (WHMM) are combined to optimize the signal, thereby improving the inspection effect of metal defects. In the test results of the relationship between the propagation distance of 316 L steel and the defect echo signal, the echo signal gradually fitted as the propagation distance increased. When the propagation distance was greater than 10 mm, the image acquisition defect signal had significant noise points. Various techniques were used to process the original echo signals of metal surface defects. The improved PCA-WHMM algorithm had significant advantages with the SNR value of the defect image increased by 13.65 % compared to PCA-WHMM. At the same time, the surface repair effects of laser surfacing 316 L metal before and after optimization were compared. The hardness, toughness, and corrosion resistance of the optimized metal were significantly improved. The proposed technological innovation combines traditional laser surfacing repair with deep learning fault diagnosis, which not only greatly improves the efficiency of fault diagnosis, but also proves that this research can effectively avoid common focus issues of laser surfacing repair technology, providing important technical reference for the application of ultrasonic technology in metal defect detection.

Automation of the process of segmentation of images of metal surface defects using the neural network U-Net

Automation of the process of segmentation of images of metal surface defects using the neural network U-Net

Product Defect Identification System

Abstract: Some uncontrollable defects will occur on the surface of metal work pieces during processing. The existence of surface defects not only affects the appearance of the finished product, but also affects the quality to a certain extent. Surface defect detection of metal work pieces can effectively improve product quality and production efficiency, and is an important link in the process of product quality control. This proposed system uses the convolutional neural network algorithm in deep learning to classify and detect metal surface defects. The surface defect recognition accuracy and defect detection rate of metal work is computed.

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.

A sub-region Unet for weak defects segmentation with global information and mask-aware loss

In recent years, diverse detection methods have been proposed to achieve highly accurate image segmentation. However, low contrast and background noise interference still pose challenges to defect detection. Existing deep learning-based methods for defect detection mostly learn rich features on the whole image. This can lead to overfitting as the parameters get bigger, and it also loses the spatial connection. In order to solve this problem, we suggest a network based on spatial sub-region feature extraction and mask-aware loss that can detect metal surface defects. In this network framework, we obtain the contextual information of each region through two streams: one branch encodes the spatial sub-region information and reduces the effect of geometric and illumination deformation under u-shape architecture. A skip-connected structure in another stream is designed to learn global information and compensate for boundary features. Next, we propose a mask-aware loss to reduce background noise interference further. Finally, we validate the effectiveness of our method on two challenging datasets, reaching 88.45% and 79.16% on the NEU-Seg and USB-Seg datasets, respectively. Also, our model is only 5.48 MB, and can achieve 172 fps for processing images with 200 × 200 size defects.

A comprehensive review on metal based active sites and their interaction with O3 during heterogeneous catalytic ozonation process: Types, regulation and authentication.

Heterogeneous catalytic ozonation (HCO) was a promising water purification technology. Designing novel metal-based catalysts and exploring their structural-activity relationship continued to be a hot topic in HCO. Herein, we reviewed the recent development of metal-based catalysts (including monometallic and polymetallic catalysts) in HCO. Regulation of metal based active sites (surface hydroxyl groups, Lewis acid sites, metal redox cycle and surface defect) and their key roles in activating O3 were explored. Advantage and disadvantage of conventional characterization techniques on monitoring metal active sites were claimed. In situ electrochemical characterization and DFT simulation were recommended as supplement to reveal the metal active species. Though the ambiguous interfacial behaviors of O3 at these active sites, the existence of interfacial electron migration was beyond doubt. The reported metal-based catalysts mainly served as electron donator for O3, which resulted in the accumulation of oxidized metal and reduced their activity. Design of polymetallic catalysts could accelerate the interfacial electron migration, but they still faced with the dilemma of sluggish Me(n+m)+/Men+ redox cycle. Alternative strategies like coupling active metal species with mesoporous silicon materials, regulating surface hydrophobic/hydrophilic properties, polaring surface electron distribution, coupling HCO process with photocatalysis and H2O2 were proposed for future research.

An Efficient Channel Attention-Enhanced Lightweight Neural Network Model for Metal Surface Defect Detection

There are problems of low model detection accuracy, low detection speed and difficulty in deploying online inspection in industrial surface defect detection relying on deep learning object detection algorithms. In order to effectively solve this problem, an efficient channel attention-enhanced lightweight neural network model named as EMV2-YOLOX is proposed in this paper. The algorithm incorporates the ECA module into the lightweight backbone extraction network MobileNetV2 to achieve adaptive adjustment of channel information weights, which can improve the extraction capability of the algorithm. The YOLOX model is also introduced to enhance the model’s identification and localization of tiny defects. The improved algorithm can guarantee the model’s accuracy and improve the model detection performance, as well as the carrying capacity of hardware devices. The experimental results show that the highest accuracy is achieved on the GCT10 and NEU public defect datasets with mean Average Precision values of 0.86 and 0.68, respectively, which is higher than the accuracy of the EMV2yoloV4 model. The parametric model number is only 10.24[Formula: see text]M in size, and the detection rate is 54.25[Formula: see text]f/s, which is the highest performance in embedded devices. EMV2-YOLOX, combined with the attention mechanism, can efficiently extract the location and semantic information of hard-to-detect defects and plays a vital role in the intelligent detection methods.

Semantic Segmentation of Metal Surface Defects and Corresponding Strategies

Facing the demand of industry for metal surface detection, this article addresses the segmentation of metal surface defects. Metal surface defect segmentation has many technical problems, such as serious imbalance in quantity and distribution, low contrast, and weak boundary information. To solve these problems, this article proposes a multi-scale attention feature fusion module, normalized mean square frequency category weight strategy, biased weight training sampling strategy, and category-boundary loss calculation strategy. The multi-scale attention feature fusion module fuses different levels of feature information to obtain defect features in images more effectively. The normalized mean square frequency category weight strategy suppresses the excessive weight of small defect categories and false positive classification. The biased weight training sampling strategy allocates different training frequencies to training samples and increases the attention to defect samples. The category-boundary loss calculation strategy imposes weights on the defect boundary and increases the attention to the boundary information. On four public datasets (Severstal, SD900, Magnetic-tile, and Crack), the proposed method achieves improvements of 7.8%, 4.0%, 18.4%, and 9.4% (absolute quantity), respectively, compared with the baseline method, while the calculation amount and number of parameters remain almost unchanged. Hence, our method ranks as state of the art.

MeDERT: A Metal Surface Defect Detection Model

Defects in various products are unavoidable because of measurement errors and equipment accuracy limitations in the production process. Recent advances in metal surface defect detection have focused on optimizing traditional methods, developing new detection techniques, and exploring deep learning-based algorithms, providing technological support to improve metal manufacturing quality and production efficiency. To ensure the highest yield rate and meet production requirements, all products must undergo defect inspections before leaving the factory. However, Traditional methods for detecting metal surface defects require a lot of manual involvement, are difficult to accurately detect small defects, are susceptible to environmental interference, and lack stability and reliability. To address this issue, we propose the MeDERT model for metal surface defect detection. Our approach involves a new Span-sensitive Texture Fusion (STF) module structure that focuses on multi-headed attention modules to recover lost details and boost inspection speed and on top of that use the Jump-sensitive detail recovery feature fusion module to ensure the validity of the extracted textures. Additionally, we introduce singular value decomposition and pretzel noise to model the noise and enhance model robustness through data augmentation. Our MeDERT model achieved state-of-the-art (SOTA) results on a specified dataset, demonstrating its effectiveness in enhancing inspection efficiency and accuracy.

Synthesis of the artificial intelligence and model-based and statistical algorithms in the classification of the metal surface defects

Synthesis of the artificial intelligence and model-based and statistical algorithms in the classification of the metal surface defects