Steel Surface Defect Detection Research Articles

Semantic Web-Based Image Super-Resolution for Enhanced Steel Surface Defect Detection

This study addresses the challenges of steel surface defect detection by introducing the ATG-SRGAN model, which integrates Semantic Web technologies with an Adaptive Transformer and Generation module. By leveraging self-attention mechanisms and residual convolution within the SRGAN framework, the model enhances image resolution, reduces noise, and uncovers finer details, achieving superior PSNR (37.17) and SSIM (0.9590) metrics. Integrated with an improved YOLOv8-ghost-p2 framework, the model also attains a mean Average Precision (mAP) of 81.2 and a recall rate of 76 in defect detection. These results demonstrate the model's potential to significantly improve automated quality control in industrial settings, highlighting the importance of Semantic Web technologies in advancing defect detection methods.

Steel Surface Defect Detection Based on YOLOv8-TLC

Steel Surface Defect Detection Based on YOLOv8-TLC

An Improved YOLOv8n Algorithm for Steel Surface Defect Detection

Due to the manufacturing process and other aspects, steel surface defects vary in shape and size, which is a difficult point for defect detection. For this reason, this paper proposes a steel surface defect detection algorithm based on YOLOv8n. The algorithm improves the C2f module of Backbone in this network by adding Context Guided Block (CG block), which can effectively extract the local features, surrounding context and global context, and integrate this information to improve the precision and accuracy of detection. Meanwhile, we also change the detection header of YOLOv8 to Detect_ASFF by introducing the Adaptive Spatial Feature Fusion (ASFF) technique. This improvement effectively filters out the conflicting information, which can allow the model to have a better adaptability at different scales. In this paper, Shape-IoU is adopted as the bounding box loss function to make up for the traditional IoU loss that ignores the intrinsic properties of the bounding box in the calculation process. After optimisation, mAP@0.5 are improved to 67.9% and mAP@0.5~0.95 are improved to 35.1%, which is 2.8% and 1.1% respectively. The improved algorithm in this paper has improved in detection accuracy, which proves the practicality and effectiveness of the algorithm in this paper.

A Lightweight Strip Steel Surface Defect Detection Network Based on Improved YOLOv8

Strip steel surface defect detection has become a crucial step in ensuring the quality of strip steel production. To address the issues of low detection accuracy and long detection times in strip steel surface defect detection algorithms caused by varying defect sizes and blurred images during acquisition, this paper proposes a lightweight strip steel surface defect detection network, YOLO-SDS, based on an improved YOLOv8. Firstly, StarNet is utilized to replace the backbone network of YOLOv8, achieving lightweight optimization while maintaining accuracy. Secondly, a lightweight module DWR is introduced into the neck and combined with the C2f feature extraction module to enhance the model’s multi-scale feature extraction capability. Finally, an occlusion-aware attention mechanism SEAM is incorporated into the detection head, enabling the model to better capture and process features of occluded objects, thus improving performance in complex scenarios. Experimental results on the open-source NEU-DET dataset show that the improved model reduces parameters by 34.4% compared with the original YOLOv8 algorithm while increasing average detection accuracy by 1.5%. And it shows good generalization performance on the deepPCB dataset. Compared with other defect detection models, YOLO-SDS offers significant advantages in terms of parameter count and detection speed. Additionally, ablation experiments validate the effectiveness of each module.

YOLO-LFPD: A Lightweight Method for Strip Surface Defect Detection.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction, slow detection speed, and insufficient datasets, YOLOv5 is improved on the basis of YOLOv5, and the YOLO-LFPD (lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, a GA genetic algorithm with OTA loss function, further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip

ABSTRACT Surface defect detection of hot-rolled steel strip is a crucial process in its production. Aiming at the problem of poor detection accuracy caused by complex shape of defect features and imbalance between object scale and feature scale, this paper proposes a multi-scale sensing and multi-dimensional feature enhancement algorithm. Firstly, a multi-branch residual aggregation module (MRAM) is proposed, which uses the interactive features of different receptive fields between layers to capture global and local texture features, improving the feature extraction capability for defects of different scales. Then, the multi-dimensional attention enhancement head (MAEH) is proposed, which integrates feature information from different prediction branches and realises feature information enhancement in scale, space and channel dimensions for the aggregated information to improve the accuracy and robustness of steel surface defect detection. The results show that the proposed algorithm achieves 82.7% mAP and 89 FPS on the NEU-DET dataset, and 73.3% mAP and 94 FPS on the GC10-DET dataset. Compared with state-of-the-art algorithms, the proposed algorithm has the highest mAP, strong generalisation ability, and good real-time performance, which proves its effectiveness in detecting hot-rolled steel strip surface defects.

Experimental Design of Steel Surface Defect Detection Based on MSFE-YOLO—An Improved YOLOV5 Algorithm with Multi-Scale Feature Extraction

Integrating artificial intelligence (AI) technology into student training programs is strategically crucial for developing future professionals with both forward-thinking capabilities and practical skills. This paper uses steel surface defect detection as a case study to propose a simulation-based teaching method grounded in deep learning. The method encompasses the entire process from data preprocessing and model training to validation analysis and innovation optimization with the goal of deepening students’ understanding of AI technology and enhancing their ability to apply it to real-world scenarios. We have designed an experimental framework that incorporates the Efficient Multi-Scale Attention (EMA) mechanism into the Backbone network. This approach helps students understand the principles of feature extraction and the core functions of attention mechanisms. Additionally, we introduced a novel architecture—Convolution 3 Dilated Convolution X (C3DX)—into the Neck network. This architecture effectively expands the network’s receptive field, improves its ability to capture multi-scale information, and thus enhances defect detection accuracy. Furthermore, the implementation of the Efficient Intersection over Union (EIoU) loss function optimizes the bounding box predictions, further increasing the model’s accuracy and robustness. Overall, the teaching design not only ensures that the content remains at the cutting edge of technology but also emphasizes its practicality and operability. This approach enables students to effectively apply theoretical knowledge to real-world engineering projects.

ECM-YOLO: a real-time detection method of steel surface defects based on multiscale convolution

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network’s robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e., multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.

Resilient machine learning for steel surface defect detection based on lightweight convolution

AbstractSteel, as a crucial material extensively used in various fields, has a critical impact on the determination of the stability and reliability of engineering structures. Nevertheless, because of inevitable factors in manufacturing, transportation, and other processes, steel may exhibit various surface defects during production and handling. To address these defects, the investigation puts forward a resilient machine-learning method for steel surface defect detection based on lightweight convolution. First, to reduce redundant features, complexity, and computational cost, the Spatial and Channel Reconstruction Convolution (ScConv) module is added before the Spatial Pyramid Pooling-Fast (SPPF) within the YOLOv8n’s backbone network. Second, in the Neck layer, lightweight convolution GSConv is used to replace the convolutional modules, and the efficient cross-stage partial network (CSP) module, VoV-GSCSP is substituted for the C2f module to alleviate the model burden while maintaining accuracy. Then, to focus on important information related to the current task, the Coordinate Attention module is added to the Neck layer. Finally, the activation function of YOLOv8n has been swapped for the Leaky Rectified Linear Unit (LeakyReLU) to effectively address issues such as gradient vanishing and overfitting. The method achieved a mean Average Precision (mAP) of 77.7% on the NEU-DET dataset, which is an improvement of 4.7% over the original YOLOv8n. Additionally, the frames per second (FPS) reached 17.36 f/s, representing a 5.79 f/s increase compared to the original YOLOv8n. On the GC10-DET dataset, mAP improves by 5.5%, with a FPS of 15.63 f/s. A plethora of experimentation on both datasets illustrates the method’s robustness, meeting the precision criteria for detecting metal defects.

A Global Feature Reused Network for Defect Detection in Steel Images

Abstract Accurate detection of surface defects for steel is essential to improve surface quality and service life. Deep learning (DL) used in steel surface defect detection can solve the problems of low efficiency and poor accuracy of traditional manual detection. The classic YOLOv5 as a DL method is used to accomplish defect detection tasks without attention mechanisms, resulting in a loss of global information. Besides, it is difficult to complete complex network detection tasks with low-configuration hardware, especially for surface defects with complex defect types and variable defect sizes. To solve these issues, this paper introduces an improved global feature reuse and hardware-aware YOLOv5 by using BoTNet, RepGhost, and EfficientRep model (BGE-YOLOv5). The multi-head self-attention layer is used to obtain global information and only part of the convolutional layers is replaced to avoid the excessive computational cost. The RepGhost model is introduced to extract the remaining feature information for feature reuse. EfficientRep is used to replace the original structure to achieve hardware-aware and to balance the detection veracity and efficiency. The distance IOU is replaced by SCYLLA-IOU to accelerate the iteration and improve stability. The results of the framework on the surface defect database (NEU-DET) show that BGE-YOLOv5 achieves a mean average precision of 79.5%, which is 10.3% greater than the baseline. The proposed BGE-YOLOv5 has a better performance in steel surface defect detection.

Research on steel surface defect detection system based on YOLOv5s-SE-CA model and BEMD image enhancement

ABSTRACT The nondestructive testing capability of steel surface defects has a significant impact on quality of industrial production. However, the single-stage detection network YOLOv5s model for steel surface defect detection is of insufficient feature extraction ability and susceptibility to noise interference in extracting surface defect images. Accordingly, a steel surface defect detection system based on YOLOv5s-SE-CA model and bi-dimensional empirical mode decomposition (BEMD) image enhancement is proposed. Firstly, the BEMD is used for image threshold denoising to improve the quality of surface defect images and achieve preprocessing of the dataset. Secondly, the squeeze and excitation network (SENet) attention mechanism is introduced into the backbone network of YOLOv5s model to adaptively adjust weights of each feature channel. Meanwhile, the coordinate attention (CA) attention mechanism is integrated to improve network model’s ability to focus on surface defects. Therefore, the YOLOv5s-SE-CA model is designed to enhance detection capability of steel surface defects. Experimental verification was carried out using the NEU-DET noisy dataset with specific evaluation indicators of image enhancement detection system. Results showed the proposed system has superior performance when compared to traditional models trained on noisy and denoising datasets and achieved more accurate detection of surface defects in steel.

AEDN-YOLO: an efficient one-stage detection network for strip steel surface defects

Steel surface defect detection is one of the key tasks in industrial production and quality control. Research on defect detection using deep learning algorithms has shown promising results. However, due to the complex backgrounds, large differences in defect sizes, and diverse defect types present in steel strip surface defect images, existing deep learning algorithms struggle to achieve precise detection. To address these challenges, this paper proposes an efficient detection model named AEDN-YOLO. Firstly, an adaptive feature extraction (AFE) module is designed, embedded into C2f to better capture irregularly shaped objects. Secondly, the Triplet Attention module is incorporated into the bottom layer of the backbone network to enhance the model’s ability to locate defect features accurately. Additionally, replace the standard convolution in the neck network with GSConv, which not only accelerates feature fusion to improve detection speed but also enlarges the model’s receptive field to enhance detection accuracy. Finally, add a small target detection layer to enhance the detection capability for tiny defects. The model achieves mAP of 81.7% and 72.7% on the NEU-DET and GC10-DET datasets, respectively, with a detection speed of 72.1 FPS. Compared to mainstream defect detection algorithms, the proposed algorithm enables accurate and efficient detection of steel surface defects.

EFS-YOLO: a lightweight network based on steel strip surface defect detection

Abstract With the advancement of deep learning technologies, industrial intelligent detection algorithms are gradually being applied in practical steel surface defect detection. Addressing the issues of high computational resource consumption and poor detection performance faced by existing models in large-scale industrial production lines, this paper proposes an EFS-YOLO (Efficient-Fast-Shared-YOLO) model based on improved YOLOv8s architecture. Firstly, the EfficientViT is employed as the feature extraction network, effectively reducing the model’s parameters and calculations while enhancing its capability to represent defect features. Secondly, the designed lightweight C2f-Faster-EffectiveSE Block (CFE-Block) was integrated into the model neck, accelerating feature fusion and better preserving detailed defect features at lower levels. Finally, the model detection head was reconstructed using the concept of shared parameters to address the high computational cost of the original detection head. Experimental results on the NEU-DET and GC10-DET datasets demonstrate that compared to the baseline model, the proposed model achieves a reduction in parameters, calculations and size by 49.5%, 62.7% and 46.9% respectively. It also exhibits an improvement in accuracy by 2.4% and 2.3% on the two datasets. The EFS-YOLO model effectively enhances precision in steel surface defect detection while maintaining lightweight characteristics, offering superior performance.

Research on Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n

Research on Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n

MAA-YOLOv8: enhanced steel surface defect detection through multi-head attention mechanism and lightweight feature fusion

In the process of industrial production, product defects often arise due to improper operations among other reasons, rendering the detection of such flaws an indispensable procedure. However, the vast array of defect types, coupled with their complex characteristics, poses ongoing challenges for contemporary defect detection algorithms within industrial settings. To solve this problem, the present study introduces an enhanced steel surface defect detection model based on the modified YOLOv8 algorithm-termed the MAA-YOLOv8 model-to augment the accuracy and practicality of the algorithm. Initially, a multi-head attention mechanism was incorporated into the C2f to bolster the feature extraction capabilities within the backbone network and diversify the attention maps. Secondly, in the neck structure, we design a multi-channel feature fusion module (McPAN) to solve the problem of balance between computational efficiency and the ability to capture useful features. A series of experiments conducted on the NEU-DET dataset reveal that the MAA-YOLOv8 model achieves a mean Average Precision (mAP) of 94.4%, representing an enhancement of 11.1% over the original YOLOv8s model. The MAA-YOLOv8 model proposed in this study substantially elevates the performance of steel surface defect detection while ensuring the speed of detection.

A high-speed YOLO detection model for steel surface defects with the channel residual convolution and fusion-distribution

Abstract Accurately and efficiently detecting steel surface defects is a critical step in steel manufacturing. However, the compromise between the detection speed and accuracy remains a major challenge, especially for steel surface defects with large variations in the scale. To address the issue, an improved you only look once (YOLO) based detection model is proposed through the reinforcement of its backbone and neck. Firstly, for the reduction of the redundant parameters and also the improvement of the characterization ability of the model, an effective channel residual structure is adopted to construct a channel residual convolution module and channel residual cross stage partial module as components of the backbone network, respectively. They realize the extraction of both the shallow feature and multi-scale feature simultaneously under a small number of convolutional parameters. Secondly, in the neck of YOLO, a fusion-distribution strategy is employed, which extracts and fuses multi-scale feature maps from the backbone network to provide global information, and then distributes global information into local features of different branches through an inject attention mechanism, thus enhancing the feature gap between different branches. Then, a model called CRFD-YOLO is derived for the steel surface defect detection and localization for the situations where both speed and accuracy are demanding. Finally, extensive experimental validations are conducted to evaluate the performance of CRFD-YOLO. The validation results indicate that CRFD-YOLO achieves a satisfactory detection performance with a mean average precision of 81.3% on the NEU-DET and 71.1% on the GC10-DET. Additionally, CRFD-YOLO achieves a speed of 161 frames per second, giving a great potential in real-time detection and localization tasks.

CFE-YOLOv8s: Improved YOLOv8s for Steel Surface Defect Detection

Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (CBiF) modules, Faster-C2f (FC) modules, and EMA-Faster-C2f (EFC) modules. Firstly, because of the potential information loss that convolutional neural networks (CNN) may encounter when dealing with miniature targets, the CBiF combines CNN with Transformer to optimize local and global features. Secondly, to address the increased computational complexity caused by the extensive use of convolutional layers, the FC uses the FasterNet block to reduce redundant computations and memory access. Lastly, the EMA is incorporated into the FC to design the EFC module and enhance feature fusion capability while ensuring the light weight of the model. CFE-YOLOv8s achieves mAP@0.5 values of 77.8% and 69.5% on the NEU-DET and GC10-DET datasets, respectively, representing enhancements of 3.1% and 2.8% over YOLOv8s, with reductions of 22% and 18% in model parameters and FLOPS. The CFE-YOLOv8s demonstrates superior overall performance and balance compared to other advanced models.

Steel surface defect detection algorithm in complex background scenarios

Detecting surface defects on steel poses a significant challenge attributed to factors such as poor contrast, diverse defect types, complex background clutter, and noise interference present in images of steel surface defects. Current detection techniques face challenges in quickly and accurately identifying defects within complex backgrounds. To address the deployment of high-precision detection models on edge devices with limited resources, particularly for identifying steel surface defects, this study introduces a Multi-Scale Adaptive Fusion (MSAF) YOLOv8n defect detection algorithm designed for complex backgrounds. This algorithm effectively balances detection speed and accuracy. Firstly, a Multi-Scale Adaptive Fusion Block (MS-AFB) is proposed for the extraction of multi-scale features. Secondly, a Dynamic Coordinate Attention Ghostconv Space Pooling Pyramid-fast Cross-stage Partial Convolutional (DCA-GSPPFCSPC) is devised to significantly improve detection accuracy. Furthermore, the detection head has been redesigned utilizing Lightweight Multi-scale Convolutional (LMSC) approach, and an Adaptive Pyramid Receptive Field Block (AP-RFB) has been introduced to improve the receptive field efficiently. Meanwhile, Normalized Weighted Distance (NWD) and Weighted Intersection over Union (WIoU) are employed as the boundary box loss functions, serving as substitutes for Complete Intersection over Union (CIoU) loss function with a ratio of 2:8. The experimental results obtained from the improved Northeastern University Defect Dataset (NEU-DET) dataset demonstrate that MSAF-YOLOv8n model, despite having 40.4 % of the parameters and 28.8 % of Floating Point Operations (FLOPs) of YOLOv8s, achieves a mAP@.5 that is 0.9 % higher than that of YOLOv8s. Additionally, MSAF-YOLOv8n demonstrates robust generalization capabilities in Pascal VOC2007, self-constructed datasets, and various other datasets. Subsequently, the model is implemented on embedded systems, namely Jeston TX2 NX and Orange Pi 5+, both of which demonstrate real-time detection capabilities.

DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection

Steel surface defect detection is crucial in manufacturing, but achieving high accuracy and real-time performance with limited computing resources is challenging. To address this issue, this paper proposes DFFNet, a lightweight fusion network, for fast and accurate steel surface defect detection. Firstly, a lightweight backbone network called LDD is introduced, utilizing partial convolution to reduce computational complexity and extract spatial features efficiently. Then, PANet is enhanced using the Efficient Feature-Optimized Converged Network and a Feature Enhancement Aggregation Module (FEAM) to improve feature fusion. FEAM combines the Efficient Layer Aggregation Network and reparameterization techniques to extend the receptive field for defect perception, and reduce information loss for small defects. Finally, a WIOU loss function with a dynamic non-monotonic mechanism is designed to improve defect localization in complex scenes. Evaluation results on the NEU-DET dataset demonstrate that the proposed DFFNet achieves competitive accuracy with lower computational complexity, with a detection speed of 101 FPS, meeting real-time performance requirements in industrial settings. Furthermore, experimental results on the PASCAL VOC and MS COCO datasets demonstrate the strong generalization capability of DFFNet for object detection in diverse scenarios.

Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n

The traditional detection methods of steel surface defects have some problems, such as a lack of feature extraction ability, sluggish detection speed, and subpar detection performance. In this paper, a YOLOv8-based DDI-YOLO model is suggested for effective steel surface defect detection. First, on the Backbone network, the extended residual module (DWR) is fused with the C2f module to obtain C2f_DWR, and the two-step approach is used to carry out the effective extraction of multiscale contextual information, and then fusing feature maps formed from the multiscale receptive fields to enhance the capacity for feature extraction. Also based on the above, an extended heavy parameter module (DRB) is added to the structure of C2f_DWR to make up for the lack of C2f’s ability to capture small-scale pattern defects between training to enhance the training fluency of the model. Finally, the Inner-IoU loss function is employed to enhance the regression accuracy and training speed of the model. The experimental results show that the detection of DDI-YOLO on the NEU-DET dataset improves the mAP by 2.4%, the accuracy by 3.3%, and the FPS by 59 frames/s compared with the original YOLOv8n.Therefore, this paper’s proposed model has a superior mAP, accuracy, and FPS in identifying surface defects in steel.