Metal Surface Defect Detection Research Articles

LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces

After the metal workpiece has been polished, there may still be small defects on the surface, which will adversely affect the quality of the product and the service life and availability of the metal in severe cases. To solve this problem, the industrial sector has been seeking more lightweight and efficient solutions. Therefore, this thesis proposes a Lightweight Adaptive Activation Convolution Network (LAACNet). Firstly, this thesis proposes a lightweight convolution module that adopts features concatenation to realize intra-channel and inter-channel information transfer and fusion. Secondly, this thesis proposes an adaptive activation convolution module with the enhanced nonlinear expression of the module, which makes the deep neural network expression more powerful. This thesis proposes a spatial channel coordinate attention module to capture the long-range dependencies between image pixels better. Finally, this thesis introduces a loss function that can optimize performance in target classification and localization tasks. Experiments were conducted on the self-made datasets Metal Surface Defect-Detection (MSD-DET) and GC10-Detection (GC10-DET), achieving Mean Average Precision 50 (mAP50) of 86.3% and 66.8%, respectively. The detection performance of this model is superior to other methods. In the ablation experiment, this thesis verified the effectiveness of each module. This thesis validated the Northeastern University-Detection (NEU-DET) dataset, achieving a mAP50 of 76.0%. The results show that LAACNet exhibits excellent robustness and generalization performance in surface defect detection. In addition, the method significantly reduces the number of model parameters, providing an effective choice for lightweight and efficient solutions.

Metallic surface defect recognition network based on global feature aggregation and dual context decoupled head

Surface defect detection is a crucial inspection phase in ensuring industrial product quality and reliability, particularly in the context of metallic manufacturing components. While existing deep learning-based approaches have demonstrated some effectiveness, the design of certain network structures still lacks consideration for industrial scenarios. This paper proposed a complex metallic surface defect detection neural network which incorporates two innovatively designed modules along with other targeted enhancements. Firstly, the spatial pyramid pooling (SPP) structure is redesigned for richer global-local information fusion. A global feature fusion and redistribution module (GFAR) is designed to fully leverage feature information across different scales. With the combination of a novel global positional attention mechanism (GPAM), GFAR is capable of distinguishing defects from complex backgrounds. Moreover, the asymmetric convolution blocks (ACB) are employed to process the final features, enhancing the representational capacity for intricate defect shapes. Subsequently, the detection head is decoupled in both spatial and task domains to addresses the problems posed by inconsistent focus between classification and regression tasks. Finally, the reassignment of loss functions is undertaken for each respective task to enhance their alignment with the specific requirements of the defect detection task. Extensive ablation experiments are conducted on two steel surface defect datasets, NEU-DET and GC10-DET, to demonstrate the effectiveness of each module. The defects detection accuracy of our method respectively reaches 80.9 % and 84.8 % for the two datasets, which outperforms the majority of state-of-the-art detection neural networks.

STFE-Net: A multi-stage approach to enhance statistical texture feature for defect detection on metal surfaces

Statistical texture features are essential for metal surface defect detection. However, low contrast and cluttered backgrounds exacerbate the intrinsic blurriness and variability of defect textures, hampering accurate identification and localization. To tackle these challenges, we propose a Statistical Texture Feature Enhancement Network (STFE-Net) based on YOLO model, a multi-stage approach based on pooling attention mechanism and cross-scale shallow feature reinforcement. Firstly, we enhance the image contrast and propose a Stacked Pooling Attention Residual (SPA-Res) module to capture better texture features; then, we embed the Shallow Feature Weighted (SFW) branches to achieve cross-layer shallow feature reinforcement and add feature layer (AFL) to extract rich semantic features; and finally, we propose an Efficient Complete Intersection over Union (ECIoU) loss function and employ four detection heads to achieve precise localization of defects. On the NEU-DET dataset, our model achieves the state-of-the-art performance. Especially, the mean Average Precision (mAP@IoU = 0.5) of our model is 82.3 %, which is 6.2 % higher than baseline network. With the input resolution of 640 × 640, STFE-Net achieves 54 FPS inference speed with a GeForce GTX 1080. In addition, the experimental results on the DAGM dataset and GC10-DET dataset demonstrate that the proposed model has excellent generalization ability.

Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection: A Comparative Study

In recent years, deep learning models have led to improved accuracy in industrial defect detection, often using variants of YOLO (You Only Look Once), due to its high performance at a low cost. However, the generalizability, fairness and bias of their outcomes have not been examined, which may lead to overconfident predictions. Additionally, the complexity added by co-occurring defects, single and multi-class defects, and the effect on training, is not taken into consideration. This study addresses these critical gaps by introducing new methodologies for analyzing dataset complexity and evaluating model fairness. It introduces the novel approach of co-occurrence impact analysis, examining how the co-occurrence of defects in sample images affects performance, and introducing new dimensions to dataset preparation and training. Its aim is to increase model robustness in the face of real-world scenarios where multiple defects often appear together. Our study also innovates in the evaluation of model fairness by adapting the disparate impact ratio (DIR) to consider the true positive rate (TPR) across different groups and modifying the predictive parity difference (PPD) metric to focus on biases present in industrial quality control. Experiments demonstrate by cross-validation that the model trained on combined datasets significantly outperforms others in accuracy without overfitting and results in increased fairness, as validated by our novel fairness metrics. Explainability also provides valuable insights on the effects of different training regimes, notably absent in prior works. This work not only advances the field of deep learning for defect detection but also provides a strategic framework for future advancements, emphasizing the need for balanced datasets and considerations of ethics, fairness, bias and generalizability in the deployment of artificial intelligence in industry.

Metal surface defect detection based on Latitude-Longitude light sources

Surface defect detection is a common engineering task, and traditional human visual inspection suffers from low efficiency, poor resolution, and a lack of traceable data. We propose a surface-topography detection system based on multicolor latitudinal and longitudinal light sources. By activating multicolor latitudinal and multicolor longitudinal light sources at the same position but at different times, decoupling and highlighting of the surface gradient magnitude and direction in the hue domain of the target surface can be achieved. We used a hot-rolled aluminum sheet as a target and calibrated the vision system, achieving recovery of the gradient magnitude and direction, which provided the necessary conditions for defect data analysis. We also used a hot-rolled aluminum sheet as the target for collecting co-located latitudinal and longitudinal images. In these images, various surface defects were highlighted relative to the background, with good differentiation between the different defects.

Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach.

The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP-Add module is added after the Concat module in the network's Head section to strengthen the network's ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model's CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry's demand for rapid and efficient detection and recognition of metal surface defects.

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%.

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%.

CFM-YOLOv5:CFPNet moudle and muti-target prediction head incorporating YOLOv5 for metal surface defect detection

Aiming at the problem of low efficiency of manual detection in the field of metal surface defect detection, a deep learning defect detection method based on improved YOLOv5 algorithm is proposed. Firstly, in the feature enhancement part, we replace the multi-head self-attention module of the standard transformer encoder with the EVC module to improve the feature extraction ability. Second, in the prediction part, adding a small target detection head can reduce the negative impact of drastic object scale changes and improve the accuracy and stability of detection. Finally, the performance of the algorithm is verified by ablation experiments and analogy experiments. The experimental results show that the improved algorithm has greatly improved mAP and FPS on the data set, and can quickly and accurately identify the types of metal surface defects, which has reference significance for practical industrial applications.

Two-Stage Metal Surface Defect Detection and Classification System Based on VAEGAN and Class-Embedding

Surface defect detection is crucial in maintaining product quality across various industries. Traditional manual inspection methods are often time-consuming and subjective, which can result in inaccuracies and higher production costs. With the use of deep learning techniques, significant advancements have been made in automating the process of surface defect detection in recent years. Moreover, deep learning includes a variety of techniques, and image recognition-based deep learning is especially relevant to our field of study, which is the main focus of this research paper.In the industrial surface defect detection field, researchers have always aimed to create a deep learning-based intelligent defect detection system that achieves near-zero defect rates while maintaining a lightweight, efficient, and cost-effective solution. However, these objectives often conflict with each other, and it is unrealistic to develop a model that can achieve all of them simultaneously. Some trade-offs must be made. If accuracy is the top priority, a large amount of defective data labeled for supervised learning is usually required. If lightweight and low cost is prioritized, a simple small model such as Auto-Encoder is usually used, along with a large number of flawless images for unsupervised learning to minimize the cost of labeling.As mentioned before, it is very difficult to design a single model that can achieve all of them simultaneously. However, present-day studies frequently center on accomplishing those tasks using a single model and rarely address the multi-model architecture. This paper presents a Surface Defect Detection and Classification System that builds on the current state-of-the-art model in the field of surface defect detection, along with the zero-shot learning (ZSL) classifier based on VAEGAN and the Variational Auto-Encoder developed by our laboratory.We have developed a Surface Defect Detection and Classification System that effectively integrates the aforementioned three models. It has been validated on a dataset of metal surface defects, yielding excellent results. This system not only achieves defect detection rates that comply with industrial standards and low false positive rates but also maintains characteristics such as lightweight, low latency, and low annotation cost. In addition to achieving the above goals, this system can also instantly identify and issue anomaly notifications when there are unseen anomalies, which is generally impossible to do with supervised learning anomaly detection models.

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 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

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. This paper 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 multi-scale 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%.

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.

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.

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.

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.

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.