A Cascaded Twin Dense Network Based Metallic Surface Defect Detection Algorithm

Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R- CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix- NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the- art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

Accurate identification of defect morphology and size distribution on metallic surfaces is crucial for manufacturing, particularly in the steel industry where such materials are widely used. This study proposes a novel lightweight architecture based on YOLOv7-tiny to address the challenges of real-time metallic surface defect detection. The original anchor-based detection head is replaced with an anchor-free mechanism to reduce missed detections of defects with extreme aspect ratios, while a logarithmic transformation-based enhancement is introduced to strengthen defect features. The backbone is replaced by the lightweight MobileNetV3-large network to reduce the number of parameters, and an Efficient Multi-Scale Attention (EMA) module is embedded into the bottleneck structure to increase target importance and suppress background interference, especially for small defects. In addition, a bidirectional feature pyramid network with adaptively spatial feature fusion (BAFPN) is integrated as the Head architecture to provide richer semantic information. The improved model was evaluated on the DAGM 2007, NEU-DET, and GC10-DET datasets, which encompass metal surface defects with varying degrees of complexity. The results demonstrate an average increase of 6.24% in mean average precision (mAP) and an inference speed over 90 FPS, confirming both the efficacy and real-time capability of the proposed method.

Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

An efficient re-parameterization feature pyramid network on YOLOv8 to the detection of steel surface defect

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.

Surface defects have a serious detrimental effect on the quality of steel. To address the problems of low efficiency and poor accuracy in the manual inspection process, intelligent detection technology based on machine learning has been gradually applied to the detection of steel surface defects. An improved YOLOv8 steel surface defect detection model called YOLOv8-MGVS is designed to address these challenges. The MLCA mechanism in the C2f module is applied to increase the feature extraction ability in the backbone network. The lightweight GSConv and VovGscsp cross-stage fusion modules are added to the neck network to reduce the loss of semantic information and achieve effective information fusion. The self-attention mechanism is exploited into the detection network to improve the detection ability of small targets. Defect detection experiments were carried out on the NEU-DET dataset. Compared with YOLOv8n from experimental results, the average accuracy, recall rate, and frames per second of the improved model were improved by 5.2%, 10.5%, and 6.4%, respectively, while the number of parameters and computational costs were reduced by 5.8% and 14.8%, respectively. Furthermore, the defect detection generalization experiments on the GC-10 dataset and SDD DET dataset confirmed that the YOLOv8-MGVS model has higher detection accuracy, better lightweight, and speed.

The classification of steel surface defects plays a very important role in analyzing their causes to improve manufacturing process and eliminate defects. However, defective samples are very scarce in actual production, so using very few samples to construct a good classifier is a challenge to be addressed. If the layer number of the model with proper depth is increased, the model accuracy will decrease (not caused by overfit), and the training error as well as the test error will be very high. This is called the degradation problem. In this paper, we propose to use feature extraction + feature transformation + nearest neighbors to classify steel surface defects. In order to solve the degradation problem caused by network deepening, the three feature extraction networks of Residual Net, Mobile Net and Dense Net are designed and analyzed. Experiment results show that in the case of a small sample number, Dense block can better solve the degradation problem caused by network deepening than Residual block. Moreover, if Dense Net is used as the feature extraction network, and the nearest neighbor classification algorithm based on Euclidean metric is used in the new feature space, the defect classification accuracy can reach 92.33% when only five labeled images of each category are used as the training set. This paper is of some guiding significance for surface defect classification when the sample number is small.

Aiming at the problems of inefficient detection caused by traditional manual inspection, unclear features and few defect sample in metal surface defect detection, an improved metal surface defect detection technology the siamese network is proposed. The proposed method constructs a deep siamese network with maximum interval, which consists of 4 convolutional layers, 4 max pooling layers and the top fully connected layer to improve the accuracy of the recognition of difficult small targets while ensuring the detection efficiency of the model. The performance of the improved metal surface defect detection technology is compared with other detection methods on the NEU surface defect database. The experimental result verify that the mean average precision of the proposed method is 96.1%, which is higher that of support vector machine, region convolutional neural network, faster region convolutional neural network, single shot multibox detector, you only look once neural network v3.

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%.

To address the limitations of traditional continuous casting billet surface defect detection methods in practical applications, such as insufficient real-time performance and low detection accuracy, this study proposes an improved YOLOv8-based billet surface defect detection model, termed LRCR-YOLOv8. First, a Lightweight Spatial Cross-Down sampling (LSCD) structure
 is introduced into the network neck, which effectively reduces model parameters and computational complexity while maintaining detection accuracy. Second, an RFCAConv module is embedded into the backbone network to enhance the capability of capturing fine grained surface features of cast billets. Finally, based on the C2f module, a C2f_RFCBAMConv structure is constructed. which consists of one 3 × 3 convolutional layer and two 1 × 1 convolutional layers, thereby further optimizing feature extraction and improving the overall detection performance. Experimental results on the publicly available NEU-DET dataset demonstrate that the proposed model achieves superior detection performance across six typical defect detection categories. Specifically, the precision, recall, and mAP reach 77.8%, 73%, and 77.2%, respectively, representing improvements of 1.0 percentage point, 0.5 percentage point, and 2.7 percentage points over the original model. These results effectively validate the practical applicability and technical advancement of the proposed method in billet surface defect detection, providing technical, support for early warning and accurate identification of surface defects in the continuous casting process.

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.

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, mAP@0.5 reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%.

In the context of resource constrained embedded terminals, the current deep learning-based technology for detecting surface defects in strip steel faces challenges with low detection accuracy and efficiency. This study introduces a lightweight convolution neural network (CNN) model for detecting surface defects of strip steel, which is based on deep learning. Through the integration of non-parameter attention mechanism SimAM, inverse residual structure, and depth-wise convolution module, a lightweight attention mobile backbone network is developed to achieve optimal feature extraction. To address the challenge of detecting small surface defects the context augmentation module is introduced to provide more information for small defects detection by using multi-scale features. To improve the efficiency of feature fusion and reduce parameter redundancy, the GVFPN neck network is proposed. The network aims to represent and deal with multi-scale features objectively while minimizing costs. The experimental results indicate that on the NEU-DET dataset, the proposed network attains an mAP of 78.8% and FPS at 97 frame/s. Moreover, the model requires only 2.39 M parameters and 3.1 G FLOPs. Compared to YOLOv5s, the proposed network reduces parameters by 65.9% and FLOPs by 80.7%, while achieving a 1.3% higher mAP and a 30 frame/s increase in detection speed. These results effectively demonstrate the effectiveness and superiority of the proposed method.

The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects.

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