Steel Surface Defect Detection Research Articles

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.

Advancements in Steel Surface Defect Detection: An Enhanced YOLOv5 Algorithm with EfficientNet Integration

Steel surface defect detection is of utmost importance for ensuring product quality, cost reduction, enhanced safety, and heightened customer satisfaction. To address the limitations of traditional steel surface defect detection algorithms, which often yielded singular detection results and suffered from high miss detection rates, we proposed an enhanced Yolov5 steel surface defect detection algorithm. In this approach, this paper employed the EfficientNet network as a replacement for the Yolov5 backbone network. Subsequently, we trained and tested this modified network on a steel surface defect dataset to mitigate the challenges associated with high miss detection rates and underperforming evaluation metrics. Our experimental findings underscored the superiority of the improved algorithm, particularly when compared to Yolov5. This enhanced algorithm exhibited substantial improvements across several key performance metrics, including Precision, Recall, mAP@0.5, parameter count, and pt file size. Noteworthy achievements included a 6.39% increase in Precision for Yolov5-EfficientNetB4, a remarkable 7.75% improvement in Recall for Yolov5-EfficientNetB0, and a 5.57% boost in mAP@0.5 for Yolov5-EfficientNetB6. Additionally, the pt file size for Yolov5-EfficientNetB0 saw a substantial 39.65% reduction, although it was important to note that the inference time for the improved algorithm increased. Among the models, Yolov5-EfficientNetB6 struck the best balance in terms of performance.

Usage of an improved YOLOv5 for steel surface defect detection

Abstract The one-stage YOLOv5 steel surface defect detection has issues such as slow operation speed, loss of defect location and semantic information of small targets, and inadequate extraction of defect features. This study proposed a defect detection algorithm with improved YOLOv5 to solve these issues. The proposed algorithm used the slim-neck layer built by three new modules instead of the neck layer in YOLOv5s to achieve a lightweight network model. In addition, the spatial perception self-attention mechanism was introduced to enhance the feature extraction capability of the initial convolutional layer without limiting the input size. The improved Atrous Spatial Pyramid Pooling was added to expand the perceptual field and capture multiscale contextual information while preventing local information loss and enhancing the relevance of long-range information. The experimental results showed that the improved YOLOv5 algorithm has a reduced model volume, significantly higher detection accuracy and speed than the traditional algorithm, and the ability to detect steel surface defects quickly and accurately.

Steel Surface Defect Detection Algorithm Based on YOLOv8

To improve the accuracy of steel surface defect detection, an improved model of multi-directional optimization based on the YOLOv8 algorithm was proposed in this study. First, we innovate the CSP Bottleneck with the two convolutions (C2F) module in YOLOv8 by introducing deformable convolution (DCN) technology to enhance the learning and expression ability of complex texture and irregular shape defect features. Secondly, the advanced Bidirectional Feature Pyramid Network (BiFPN) structure is adopted to realize the weight distribution learning of input features of different scales in the feature fusion stage, allowing for more effective integration of multi-level feature information. Next, the BiFormer attention mechanism is embedded in the backbone network, allowing the model to adaptively allocate attention based on target features, such as flexibly and efficiently skipping non-critical areas, and focusing on identifying potentially defective parts. Finally, we adjusted the loss function from Complete-Intersection over Union (CIoU) to Wise-IoUv3 (WIoUv3) and used its dynamic non-monotony focusing property to effectively solve the problem of overfitting the low quality target bounding box. The experimental results show that the mean Average Precision (mAP) of the improved model in the task of steel surface defect detection reaches 84.8%, which depicts a significant improvement of 6.9% compared with the original YOLO8 model. The improved model can quickly and accurately locate and classify all kinds of steel surface defects in practical applications and meet the needs of steel defect detection in industrial production.

A lightweight image-level segmentation method for steel surface defects based on cross-layer feature fusion

Steel is widely used in the aerospace, machinery and automotive industries. Surface defects not only have a negative impact on the appearance of steel but also significantly reduce its wear resistance, high temperature resistance, corrosion resistance and fatigue strength. Therefore, the detection of steel surface defects is very important to improve the quality of steel production. The limited availability of surface defect samples in the industrial sector poses significant challenges for the accurate detection of defects in high-quality materials. In addition, the existing defect detection model is highly complex and not easy to deploy. To solve this problem, a lightweight defect detection network suitable for steel defects is proposed. The cross-layer feature fusion (CFF) in the design enables effective utilisation of multi-layer semantic features, facilitating the detection of small defects in steel. Secondly, a new loss function is designed to make up for the problems of small data volume and uneven data distribution. The experimental results demonstrate that the steel surface defect detection method proposed in this paper achieves the highest detection performance on widely used public datasets such as RSDDS, NEUS and NRSD-CR(test), while maintaining the lowest model complexity.

A light weight multi-scale feature fusion steel surface defect detection model based on YOLOv8

In industrial production, the steel surface may incur different defects owing to the influence of external factors, thereby affecting the performance of steel. With the increasing requirements for steel quality, achieving efficient detection of steel surface defects is a difficult problem that urgently needs to be solved. Traditional steel surface defect detection methods are limited by poor detection performance and slow detection speed. Therefore, a model named LMS-YOLO, based on YOLOv8, is proposed in this paper for achieving efficient steel surface defect detection. Firstly, in backbone, the light weight multi-scale mixed convolution (LMSMC) module is designed to fuse with C2f to obtain C2f_LMSMC, so as to extract the features of different scales for fusion and achieve the light weight of the network. Meanwhile, the proposed efficient global attention mechanism was added to backbone to enhance cross dimensional information interaction and feature extraction capabilities, and to achieve a more efficient attention mechanism. In neck, using channel tuning to achieve better cross scale fusion in BiFPN. Finally, the model uses three independent decoupled heads for regression and classification, and replaces CIoU with NWD as the regression loss to enhance the effect of detecting small scale defects. The experimental results showed that LMS-YOLO achieved 81.1 mAP and 61.3 FPS on NEU-DET, 80.5 mAP and 61.3 FPS on GC10-DET, respectively. The mAP increased by 2.8 and 4.7 compared to YOLOv8, and decreased by 17.4% in floating point operations (GFLOPs) and 34.2% in parameters (Params), which indicates that the model proposed in this paper has a better comprehensive performance compared with other methods in steel surface defect detection.

CGTD-Net: Channel-Wise Global Transformer-Based Dual-Branch Network for Industrial Strip Steel Surface Defect Detection

CGTD-Net: Channel-Wise Global Transformer-Based Dual-Branch Network for Industrial Strip Steel Surface Defect Detection

MPFANet: a multipath feature aggregation network for steel surface defect detection

As a crucial component of product quality surveillance in the industrial manufacturing field, surface defect detection plays a pivotal role in achieving industrial automation. Although numerous defect detection methods have been widely used in steel production, complicated defect characteristics still severely affect the accuracy and efficiency of defect detection methods. To solve this problem, we propose a multi-path feature aggregation network that can significantly improve steel defect detection performance. Firstly, we design an efficient multi-path aggregation feature extraction module combining gradient path with data path propagation design paradigm as the backbone feature extraction unit based on YOLOv5. Secondly, in the branch path of the backbone, deformable convolution is introduced to expand the effective receptive field and enhance the capability of extracting irregularly distributed defect features. Furthermore, we propose a chained feature enhanced pooling module that refines feature information to obtain feature maps containing more complicated defect details in the pooling layer. Experimental results on the publicly available steel surface defect datasets show that our proposed model achieves a mean average precision of 81.9 % on Northeastern University-DET and 72.1 % on GC10-DET improving by 4.6 % and 3.4 % over the baseline, respectively. The proposed defect detection network can achieve a balance between accuracy and efficiency, and meet the demand in practical production. The detection speed of our work reaches 41.1 frames per second (FPS) and 40.0 FPS on the above two datasets, which demonstrates that our method has achieved comprehensive performance in steel surface defect detection.

A steel surface defect detection model based on graph neural networks

Steel is an indispensable raw material in the construction industry. To avert catastrophic events such as building collapse, it is essential to detect minute defects on steel surfaces during production. However, this has been a persistent challenge due to the minuscule and dense nature of these defects. To this end, we propose an efficient defect detector called Vision Grapher with Hadamard, which employs a novel attention mechanism (HDmA) to establish local-to-local relationships within an image and integrates global relationships by graph convolution. With the HDmA module, we can not only fuse information under the same field of view, but also under different fields of view, which significantly enhances the richness of the acquired features. In addition, compared to convolutional neural networks, graph neural networks can utilize the contextual information in the image more effectively and resulting in better performance. We evaluate our model on the NEU-DET and GC-10 benchmark datasets, which encompass six and ten types of defects on the surfaces of hot-rolled and cold-rolled steel, and our model achieves a mean average precision of 79.04% and 66.93% on the two datasets, respectively. The results demonstrate that our model significantly improves the accuracy of defect detection compared to existing methods.

Steel Surface Defect Detection Method Based on Improved YOLOX

Steel Surface Defect Detection Method Based on Improved YOLOX

Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection

Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection

Steel Surface Defect Detection Method Based on Improved YOLOv8

This research aims to improve the accuracy and efficiency of steel surface defect detection and address the current insufficient algorithm performance in steel defect detection, especially the challenges in multi-scale feature extraction and parameter efficiency. Based on the improved YOLOv8 network structure, three key improvement points are introduced: the DBB module optimizes multi-scale feature extraction, the MSCA attention mechanism enhances the model's defect detection accuracy, and the optimized GDetect module reduces the amount of parameters and calculations. Extensive experiments and comparative analysis using the NEU-DET data set verify the effectiveness of the improved method. Ablation experiments show that using DBB or MSCA alone has limited performance improvement, but when MSCA and GDetect are combined, the model has significant improvements in Precision, Recall, and mAP50 indicators. Comparative experiments show that the performance of this algorithm is close to or even surpasses some more complex models while remaining relatively lightweight. The average accuracy mean analysis for six defect categories also shows the superior performance of this algorithm for various types of defects. The steel surface defect detection method proposed in this study based on the improved YOLOv8 network has made significant progress in the field of steel defect detection. The improved method improves detection accuracy, solves common problems, and achieves high performance while maintaining lightweight, providing an efficient and feasible solution for steel surface defect detection.

YOLOv5-CD: Strip steel surface defect detection method based on coordinate attention and a decoupled head

The ability to detect strip steel surface defects is crucial for ensuring the quality of items made from that material. A YOLOv5-CD algorithm is proposed to overcome the problems of insufficient feature extraction ability, low detection accuracy, and slow convergence rate of the strip steel surface defect detection technique in an industrial scenario. First, a coordination attention mechanism is added to the backbone network. This strategy embeds positional information into channel attention, addressing the issue of positional information loss caused by global pooling and effectively improving the model's feature extraction capacity. Second, the head component uses a decoupled head detector. By separating the classification and regression tasks, this approach effectively increases the model's detection precision and convergence rate. According to the experimental findings, the mAP of the YOLOv5-CD algorithm on the NEU-DET dataset is 80.6 %, 5.8 % higher than that of the original YOLOv5 method. Additionally, the algorithm's convergence speed is faster and better able to meet the demands of actual industrial production.

Optimization Algorithm for Steel Surface Defect Detection Based on PP-YOLOE

The fast and accurate detection of steel surface defects has become an important goal of research in various fields. As one of the most important and effective methods of detecting steel surface defects, the successive generations of YOLO algorithms have been widely used in these areas; however, for the detection of tiny targets, it still encounters difficulties. To solve this problem, the first modified PP-YOLOE algorithm for small targets is proposed. By introducing Coordinate Attention into the Backbone structure, we encode channel relationships and long-range dependencies using accurate positional information. This improves the performance and overall accuracy of small target detection while maintaining the model parameters. Additionally, simplifying the traditional PAN+FPN components into an optimized FPN feature pyramid structure allows the model to skip computationally expensive but less relevant processes for the steel surface defect dataset, effectively reducing the computational complexity of the model. The experimental results show that the overall average accuracy (mAP) of the improved PP-YOLOE algorithm is increased by 4.1%, the detection speed is increased by 2.06 FPS, and the accuracy of smaller targets (with a pixel area less than 322) that are more difficult to detect is significantly improved by 13.3% on average, as compared to the original algorithm. The detection performance is also higher than that of the mainstream target detection algorithms, such as SSD, YOLOv3, YOLOv4, and YOLOv5, and has a high application value in industrial detection.

Strip steel surface defect detection based on lightweight YOLOv5.

Deep learning-based methods for detecting surface defects on strip steel have advanced detection capabilities, but there are still problems of target loss, false alarms, large computation, and imbalance between detection accuracy and detection speed. In order to achieve a good balance between detection accuracy and speed, a lightweight YOLOv5 strip steel surface defect detection algorithm based on YOLOv5s is proposed. Firstly, we introduce the efficient lightweight convolutional layer called GSConv. The Slim Neck, designed based on GSConv, replaces the original algorithm's neck, reducing the number of network parameters and improving detection speed. Secondly, we incorporate SimAM, a non-parametric attention mechanism, into the improved neck to enhance detection accuracy. Finally, we utilize the SIoU function as the regression prediction loss instead of the original CIoU to address the issue of slow convergence and improve efficiency. According to experimental findings, the YOLOv5-GSS algorithm outperforms the YOLOv5 method by 2.9% on the NEU-DET dataset and achieves an average accuracy (mAP) of 83.8% with a detection speed (FPS) of 100 Hz, which is 3.8 Hz quicker than the YOLOv5 algorithm. The proposed model outperforms existing approaches and is more useful, demonstrating the efficacy of the optimization strategy.

Steel Surface Defect Detection Based on SSAM-YOLO

The defect inspection of the steel surface is crucial to modern manufacturing and highly depends on inefficient manual work. The emergence of deep learning has prompted the development of automated defect detection methods, but the current methods perform badly in the detection of the crazing and rolled-in scale-two types of defects on steel surfaces. The difficulty in the detection of crazing and rolled-in scale is mainly due to the similarity between object regions and background regions. Based on this, the authors propose a supervised spatial-attention module (SSAM). It introduces a priori knowledge compared to the traditional spatial attention mechanism, which can enhance the supervision of relevant parameters in the attention mechanism module during network training. Finally, they introduced the SSAM to the YOLOv5 and got the SSAM-YOLO. The test result on the NEU-DET dataset shows that the proposed method has better detection accuracy, achieving improvements of 7.3% and 3.02% on the AP@0.5 for the crazing and rolled-in scale. The method also outperforms the comparative main stream algorithms for steel surface defect detection, verifying the effectiveness of our algorithm.

Improved YOLOv5 Network for Steel Surface Defect Detection

Steel surface defect detection is crucial for ensuring steel quality. The traditional detection algorithm has low detection probability. This paper proposes an improved algorithm based on the YOLOv5 model to enhance detection probability. Firstly, deformable convolution is introduced in the backbone network, and a traditional convolution module is replaced by deformable convolution; secondly, the CBAM attention mechanism is added to the backbone network; then, Focal EIOU is used instead of the CIOU loss function in YOLOv5; lastly, the K-means algorithm is used to cluster the Anchor box, and the Anchor box parameters that are more suitable for this paper are obtained. The experimental results show that using deformable convolution instead of traditional convolution can get more feature information, which is more conducive to the learning of the network. This paper uses the CBAM attention mechanism, and the heat map of the attention mechanism shows that the CBAM attention mechanism is beneficial for feature extraction. Focal EIOU is optimized in high and wide loss compared with the CIOU loss function, which accelerates the convergence of the model. The Anchor box is more favorable for feature extraction. The improved algorithm achieved a detection probability of 78.8% in the NEU-DET dataset, which is 4.3% better than the original YOLOv5 network, and the inference time of each image is only increased by 1 ms; therefore, the optimized algorithm proposed in this paper is effective.

A deep learning model for steel surface defect detection

Industrial defect detection is a hot topic in the field of computer vision. It is a challenging task due to complex features and many categories of industrial defects. In this paper, a deep learning model based on the multiscale feature extraction module is introduced for steel surface defect detection. The main focus on the feature extraction capability of the model and feature fusion capability to improve the accuracy of the model for steel surface defect detection. First, to improve the feature extraction ability of the model, a multiscale feature extraction (MSFE) module is introduced. The MSFE module can effectively extract multiscale features through three branches that have different convolution kernel sizes. Second, an efficient feature fusion (EFF) module is proposed to optimize feature fusion by adding features from the backbone network to the neck network. Third, this paper puts forward a new Bottleneck module by reducing the normalization layer and activation function in the original Bottleneck module. Finally, the backbone network is deepened to further enhance the feature extraction ability of the model. Extensive experiments are conducted on the public NEU-DET dataset. The experimental results validate the effectiveness of the designed modules and the proposed model. Compared with other state-of-the-art methods, the proposed model achieves optimal accuracy(73.08% mAP@0.5) while maintaining a small number of parameters.

EFD-YOLOv4: A steel surface defect detection network with encoder-decoder residual block and feature alignment module

Surface defect detection is often required in industrial production process. However, due to the large variation in defect scale, low contrast between the background and defect target, and noise interference, the task of defect detection in industrial environments is still challenging. This paper proposes an enhanced-feature-detection YOLOv4 model for steel surface defect detection, named EFD-YOLOv4. Firstly, an augmented path consisting of a convolutional encoder-decoder module is developed for the residual block to enhance the learned representations. A multi-scale module based on hierarchical residual-like connections is applied to enlarge the receptive fields. Then, a feature alignment module with an attention mechanism is designed for feature misalignment in feature fusion. Finally, three decoupled heads are adopted to output classification and regression results independently. The experimental results show that this method achieves 79.88 mAP on the NEU-DET dataset and 54.65 mAP on the GC10-DET dataset. The proposed method has better performance on steel surface defect detection tasks.

Multi-Scale Lightweight Neural Network for Steel Surface Defect Detection

Defect classification is an important aspect of steel surface defect detection. Traditional approaches for steel surface defect classification employ convolutional neural networks (CNNs) to improve accuracy, typically by increasing network depth and parameter count. However, this approach overlooks the significant memory overhead of large models, and the incremental gains in accuracy diminish as the number of parameters increases. To address these issues, a multi-scale lightweight neural network model (MM) is proposed. The MM model, with a fusion encoding module as its core, constructs a multi-scale neural network by utilizing the Gaussian difference pyramid. This approach enhances the network’s ability to capture patterns at different resolutions while achieving superior model accuracy and efficiency. Experimental results on a dataset from a hot-rolled strip steel plant demonstrate that the MM network achieves a classification accuracy of 98.06% in defect classification tasks. Compared to networks such as ResNet-50, ResNet-101, VGG, AlexNet, MobileNetV2, and MobileNetV3, the MM model not only reduces the number of model parameters and compresses model size but also achieves better classification accuracy.