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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection. [ABSTRACT FROM AUTHOR]

Light guide plate (LGP) is a key component of liquid crystal display (LCD) display systems, so its quality directly affects the display effect of LCD. However, LGPs have complex background texture, low contrast, varying defect size and numerous defect types, which makes realizing efficient and accuracy-satisfactory surface defect automatic detection of LGPS still a big challenge. Therefore, combining its optical properties, dot distribution, defect imaging characteristics and detection requirements, a surface defect detection algorithm based on LGP-YOLO for practical industrial applications is proposed in this paper. To enhance the feature extraction ability of the network without dimensionality reduction, expand the effective receptive field and reduce the interference of invalid targets, we built the receptive field module (RFM) by combining the effective channel attention network (ECA-Net) and reviewing large kernel design in CNNs (RepLKNet). For the purpose of optimizing the performance of the network in downstream tasks, enhance the network's expression ability and improve the network's ability of detecting multi-scale targets, we construct the small detection module (SDM) by combining space-to-depth non-strided convolution (SPDConv) and omini-dimensional dynamic convolution (ODConv). Finally, an LGP defect dataset is constructed using a set of images collected from industrial sites, and a multi-round experiment is carried out to test the proposed method on the LGP detect dataset. The experimental results show that the proposed LGP-YOLO network can achieve high performance, with mAP and F1-score reaching 99.08% and 97.45% respectively, and inference speed reaching 81.15 FPS. This demonstrates that LGP-YOLO can strike a good balance between detection accuracy and inference speed, capable of meeting the requirements of high-precision and high-efficiency LGP defect detection in LGP manufacturing factories. [ABSTRACT FROM AUTHOR]

Abstract Light guide plate (LGP) is a key component of liquid crystal display (LCD) display systems, so its quality directly affects the display effect of LCD. However, LGPs have complex background texture, low contrast, varying defect size and numerous defect types, which makes realizing efficient and accuracy-satisfactory surface defect automatic detection of LGPS still a big challenge. Therefore, combining its optical properties, dot distribution, defect imaging characteristics and detection requirements, a surface defect detection algorithm based on LGP-YOLO for practical industrial applications is proposed in this paper. To enhance the feature extraction ability of the network without dimensionality reduction, expand the effective receptive field and reduce the interference of invalid targets, we built the receptive field module (RFM) by combining the effective channel attention network (ECA-Net) and reviewing large kernel design in CNNs (RepLKNet). For the purpose of optimizing the performance of the network in downstream tasks, enhance the network's expression ability and improve the network’s ability of detecting multi-scale targets, we construct the small detection module (SDM) by combining space-to-depth non-strided convolution (SPDConv) and omini-dimensional dynamic convolution (ODConv). Finally, an LGP defect dataset is constructed using a set of images collected from industrial sites, and a multi-round experiment is carried out to test the proposed method on the LGP detect dataset. The experimental results show that the proposed LGP-YOLO network can achieve high performance, with mAP and F1-score reaching 99.08% and 97.45% respectively, and inference speed reaching 81.15 FPS. This demonstrates that LGP-YOLO can strike a good balance between detection accuracy and inference speed, capable of meeting the requirements of high-precision and high-efficiency LGP defect detection in LGP manufacturing factories.

Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects. [ABSTRACT FROM AUTHOR]

Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection.

The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing. However, the defects exhibit low contrast, different textures and geometric structures, and several defects making the surface defect detection more difficult. In this paper, a pixel-wise detection framework based on convolutional neural network (CNN) for strip steel surface defect detection is proposed. First we extract the salient features by a pre-trained backbone network. Secondly, contextual weighting module, with different convolutional kernels, is used to extract multi-scale context features to achieve overall defect perception. Finally, the cross integrate is employed to make the full use of these context information and decoded the information to realize feature information complementation. The experimental results of this study demonstrate that the proposed method outperforms against the previous state-of-the-art methods on strip steel surface defect dataset (MAE: 0.0396; Fβ: 0.8485).

Surface defect detection is very important in manufacturing systems to ensure high quality products. Unlike manual inspections under human supervision, automatic surface defect detection is both efficient and highly accurate. In this study, a Result Weighting-based Resnet Feature Pyramid Network (SA-RÖPA) model has been developed for automatic pixel-level surface defect detection. In the first stage of the proposed model, the pre-trained Resnet50 network was used, and feature maps were extracted from the different levels of this network. In the second stage, Feature Pyramid Model was applied to these feature maps in order to hierarchically share important information in defect detection. In the third stage, 4 different error detection results were obtained by using these feature maps. In the last stage, four different results obtained using the developed Result Weighting (SA) module were effectively combined. The proposed SA-ROPA model has been tested with MT, MVTex-Doku, and AITEX datasets, which are widely used in defect detection studies. In experimental studies, the mIoU value obtained for the MT and AITEX datasets using the proposed model was calculated as 79.92%, 76.37%, and 82.72%, respectively. These results have shown that the proposed SA- ROPA model is more successful than other state-of-the-art models.

Artificial intelligence (AI)-empowered defect detection has emerged as a promising solution for enhancing quality control in manufacturing. While prevalent object detection-based methods have achieved competitive performance, they do carry inherent limitations that necessitate further refinement prior to their practical application in online surface defect detection. This study introduces an efficient online surface defect detection method that makes predictions on the presence of defects based on image-level labels. The method leverages the multiple instance learning (MIL) framework, and utilizes convolutional neural network (CNN) as feature extractor. Extensive experiments are conducted on two real-world datasets to evaluate the method with a custom CNN and Resnet50 as feature extractors (referred to as MIL-CNN and MIL-Resnet50). The results demonstrate the superiority of the proposed method compared with the well-established benchmark methods, especially highlighting the advantage of MIL-Resnet50. Without requiring fine-grained labeling, MIL-Resnet50 enhances F1-macro by 2.5% and 1.5% within the two datasets compared to the second-ranking. Notably, it excels in detecting small-object defects. It also exhibits advantages in terms of detection speed, and are lightweight, making it easy to deploy even in resource-limited scenarios. Additionally, MIL-Resnet50 exhibits the capability to provide approximate defect localization through feature maps. These findings highlight the significant potential of the proposed method within industrial applications. [ABSTRACT FROM AUTHOR]

A fresh-cut cauliflower surface defect detection and classification model based on a convolutional neural network with transfer learning is proposed to address the low efficiency of the traditional manual detection of fresh-cut cauliflower surface defects. Four thousand, seven hundred and ninety images of fresh-cut cauliflower were collected in four categories including healthy, diseased, browning, and mildewed. In this study, the pre-trained MobileNet model was fine-tuned to improve training speed and accuracy. The model optimization was achieved by selecting the optimal combination of training hyper-parameters and adjusting the different number of frozen layers; the parameters downloaded from ImageNet were optimally integrated with the parameters trained on our own model. A comparison of test results was presented by combining VGG19, InceptionV3, and NASNetMobile. Experimental results showed that the MobileNet model's loss value was 0.033, its accuracy was 99.27%, and the F1 score was 99.24% on the test set when the learning rate was set as 0.001, dropout was set as 0.5, and the frozen layer was set as 80. This model had better capability and stronger robustness and was more suitable for the surface defect detection of fresh-cut cauliflower when compared with other models, and the experiment's results demonstrated the method's feasibility. [ABSTRACT FROM AUTHOR]

Metallic surface defect detection is critical to ensure the quality of industrial products. Recently, human-advanced surface defect detection algorithms have been proposed. Most of these algorithms rely on convolutional neural networks (CNN) and an anchoring scheme. However, a convolution unit only samples the input feature maps at fixed shapes and locations. Similarly, a set of anchors are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression. Therefore, we propose an adaptive convolution and anchor network for metallic surface defect detection, named ACA-Net. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which mainly consists of adaptive convolution and an adaptive anchor. Firstly, an adaptive convolution module (ACM) is designed, which adaptively determines the location and shape of each convolution unit. In addition, a multi-scale feature adaptive fusion (MFAF) is proposed, which is used in ACM to extract and integrate multi-scale features. Then, an adaptive anchor module (AAM) is proposed to yield more suitable anchor boxes by adaptively adjusting shapes. Extensive experiments on NEU-DET dataset and GC10 dataset validate the performance of the proposed approach. ACA-Net achieves 1.8% on NEU-DET dataset higher Average Precision (AP) than GA-RetinaNet. Furthermore, the proposed ACA module is also adopted in GA-Faster R-CNN, improving the AP by 1.2% on NEU-DET dataset. [ABSTRACT FROM AUTHOR]

Surface defect detection is a critical task in product quality assurance for manufacturing lines. The deep learning-based methods recently developed for defect detection are typically trained using a supervised learning strategy and large defect sample sets. Conventional methods often require additional pixel-level labeling or bounding boxes to predict the location of defects. However, the number of required samples and the time-intensive annotation process limits the practical use of these algorithms. As such, this study proposes a weakly supervised detection framework in which a CNN model is trained to identify surface cracks in motor commutators. The model was trained using small subsets of defect samples (~5-30) and does not require a pre-trained network. This approach consists of localization and decision networks that simultaneously predict both the location and probability of defects. A new loss function was also developed to identify abnormal regions in a sample with accessible image-level labels. A collaboration learning strategy was then applied to utilize the loss function and compensate for imbalances at the pixel level. Experimental results using a small number of image-level training labels from a real industrial dataset exhibited a 99.5% recognition accuracy, which is comparable to relevant methods using pixel-level labels.

This article proposes a fast and accurate network for surface defect detection, termed SDDNet. SDDNet mainly addresses two challenging issues—large texture variation and small size of defects—by introducing two modules: feature retaining block (FRB) and skip densely connected module (SDCM). FRB fuses multiple pyramidal feature maps with different resolutions and is plugged on the top of pooling layers, aiming to preserve the texture information, which may be lost because of downsampling. SDCM is designed to propagate the fine-grained details from low- to high-level feature maps for better prediction of defects, especially small defects. Extensive experiments conducted on the publicly available data sets NEU-DET (88.8% mAP), DAGM (99.1% mAP), and Magnetic-Tile (93.4% mAP) have demonstrated the effectiveness of the proposed SDDNet and its feasibility for real-time industrial applications. [ABSTRACT FROM AUTHOR]

Surface defect detection is very important for the quality control of product and routine maintenance of facilities, but it is still a big challenge due to the diversity and complexity of defects and environmental factors. To improve the accuracy of defect detection, we proposed a pixel-level segmentation network based on deep feature fusion for surface defect detection. The network adopts encoder–decoder structure, and it extracts low-, middle-, and high-level features via ResNet50 first. Second, by fusing adjacent feature maps at all levels and integrating the highest level feature map, multilevel feature aggregation module makes all feature maps contain context information and more details of defects. Then, multibranch decoder adopts attention modules and a multibranch structure to recover the details of defects gradually and improve the accuracy of defects segmentation. Finally, the segmentation result is produced by fusion of all branches outputs. We have evaluated the proposed network on three public data sets: MT, RSDD, and CFD. The results indicate that our proposed method outperforms the other compared methods in terms of F-measure and intersection of union (MT:73.7%, RSDD:85%, CFD:60.1%). [ABSTRACT FROM AUTHOR]

A fresh-cut cauliflower surface defect detection and classification model based on a convolutional neural network with transfer learning is proposed to address the low efficiency of the traditional manual detection of fresh-cut cauliflower surface defects. Four thousand, seven hundred and ninety images of fresh-cut cauliflower were collected in four categories including healthy, diseased, browning, and mildewed. In this study, the pre-trained MobileNet model was fine-tuned to improve training speed and accuracy. The model optimization was achieved by selecting the optimal combination of training hyper-parameters and adjusting the different number of frozen layers; the parameters downloaded from ImageNet were optimally integrated with the parameters trained on our own model. A comparison of test results was presented by combining VGG19, InceptionV3, and NASNetMobile. Experimental results showed that the MobileNet model’s loss value was 0.033, its accuracy was 99.27%, and the F1 score was 99.24% on the test set when the learning rate was set as 0.001, dropout was set as 0.5, and the frozen layer was set as 80. This model had better capability and stronger robustness and was more suitable for the surface defect detection of fresh-cut cauliflower when compared with other models, and the experiment’s results demonstrated the method’s feasibility.

Metallic surface defect detection is critical to ensure the quality of industrial products. Recently, human-advanced surface defect detection algorithms have been proposed. Most of these algorithms rely on convolutional neural networks (CNN) and an anchoring scheme. However, a convolution unit only samples the input feature maps at fixed shapes and locations. Similarly, a set of anchors are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression. Therefore, we propose an adaptive convolution and anchor network for metallic surface defect detection, named ACA-Net. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which mainly consists of adaptive convolution and an adaptive anchor. Firstly, an adaptive convolution module (ACM) is designed, which adaptively determines the location and shape of each convolution unit. In addition, a multi-scale feature adaptive fusion (MFAF) is proposed, which is used in ACM to extract and integrate multi-scale features. Then, an adaptive anchor module (AAM) is proposed to yield more suitable anchor boxes by adaptively adjusting shapes. Extensive experiments on NEU-DET dataset and GC10 dataset validate the performance of the proposed approach. ACA-Net achieves 1.8% on NEU-DET dataset higher Average Precision (AP) than GA-RetinaNet. Furthermore, the proposed ACA module is also adopted in GA-Faster R-CNN, improving the AP by 1.2% on NEU-DET dataset.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent years have witnessed the widespread research of the surface defect detection technology based on machine vision, which has spawned various effective detection methods. In particular, the rise of deep learning has allowed the surface defect detection technology to develop further. However, these methods based on deep learning still have some drawbacks. For example, the size of the sample data is not large enough to support deep learning; the location and recognition of surface defects are not accurate enough; the real-time performance of segmentation and classification is not satisfactory. In the context, this paper proposes an end-to-end convolutional neural network model: the pixel-wise segmentation and image-wise classification network (PSIC-Net). With the innovative design of a three-stage network structure, improved loss function and a two-step training mode, PSIC-Net can accurately and quickly segment and classify surface defects with a small dataset of training data. This model was evaluated with three public datasets, and compared with the most advanced defect detection methods. All the performance metrics prove the effectiveness and advancement of PSIC-Net.

In industrial production, surface defect detection algorithms based on convolutional neural networks have been widely studied to improve production quality. However, for practical applications, there are still many issues to be solved, such as the complexity and diversity of defect categories, the difficulty of obtaining defect samples, and the difficulty of existing algorithms in accurately segmenting defects. To solve these issues, we present an effective defect segmentation network based on visual attention and visual perception termed EDSV-Net. Specifically, we use ResNet18 as the backbone network in EDSV-Net. Then a multi-scale feature extraction (MSFE) module is introduced to enhance the scale invariance of high-level features and the diversity of contextual features. In addition, a spatial attention (SA) model combined with a channel attention (CA) model is applied to low level features and MSFE features, respectively, to extract more effective spatial and semantic information. Moreover, a depthwise separable convolution is introduced to reduce the network complexity. Finally, due to the issues of existing defect detection algorithms ignoring structural similarity and defects being difficult to obtain, we design a balanced defect and structural measure loss function. Meanwhile, we propose a structural similarity measure, which combines the pixel similarity for evaluation. EDSV-Net only requires no more than 60 random abnormal samples to obtain accurate segmentation results and the real-time performance meets the requirements of actual industrial production. Based on three challenging real-world defect datasets, the results of the evaluation demonstrate that EDSV-Net outperforms seven state-of-the-art methods on accuracy and real-time performance. • An efficient, general and real-time surface defect segmentation network is designed. • An attention combination mechanism for high and low feature fusion is proposed. • A novel loss function combining pixel and structural similarity measures is proposed. • A structural similarity evaluation method is proposed for surface defect segmentation. • The network detection speed meets the industrial real-time requirements. [ABSTRACT FROM AUTHOR]

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection.

Surface defect detection remains a challenging task due to issues such as inconspicuous targets, significant variations among identical defects, and minimal differences between distinct defects. To address these challenges, a Fine Coordinate Attention (FCA) block is proposed in this paper, which encodes both average and salient information in two coordinate directions, so that the spatial dependence can be captured and the long-range interaction can be achieved. And such localization-friendly information is crucial for industrial surface defect images with subtle targets. Specifically, the FCA block can recalibrate feature maps of a surface defect image through three steps: coordinate information aggregation, cross-dimension interaction, and attention generation. It can be embedded into any convolutional neural network (CNN) structure to improve performance. Additionally, two resistance spot welding (RSW) surface defect datasets are published in this paper: an image classification dataset RSW-C and an object detection dataset RSW-D. Experimental results for image classification and object detection demonstrate that the FCA block outperforms existing attention mechanisms. The code is available at , while the two RSW datasets can be found at www.kaggle.com/datasets/alfredzimmer/rswdatasets. [ABSTRACT FROM AUTHOR]

Surface defect detection is an extremely crucial step to ensure the quality of industrial products. Nowadays, convolutional neural networks (CNNs) based on encoder–decoder architecture have achieved tremendous success in various defect detection tasks. However, the intrinsic locality of convolution prevents them from modeling long-range interactions explicitly, making it difficult to distinguish pseudo-defects in cluttered backgrounds. Recent transformers are especially skilled at learning global image dependencies, but with limited local structural information for the refined defect location. To overcome the above limitations, we incorporate CNN and transformer into an efficient hybrid transformer architecture for defect detection, termed Defect Transformer (DefT), to capture local and non-local relationships collaboratively. Specifically, in the encoder module, a convolutional stem block is firstly adopted to retain more spatial details. Then, the patch aggregation blocks are used to generate multi-scale representation with four hierarchies, each of them is followed by a series of DefT blocks, which respectively include a locally position-aware block for local position encoding, a lightweight multi-pooling self-attention to model multi-scale global contextual relationships with good computational efficiency, and a convolutional feed-forward network for feature transformation and further local information learning. Finally, a simple but effective decoder module is constructed to gradually recover spatial details from the skip connections in the encoder. Extensive experiments on three datasets demonstrate the superiority and efficiency of our method compared with other deeper and complex CNN- and transformer-based networks. • We study the benefits of vision transformer for pixel-wise defect detection tasks. • We propose a defect detection architecture by porting the properties of CNNs into transformer. • We provide a simpler but better defect detection baseline and can motivate further research. • We conduct extensive experiments to show the superiority and generalization of our method. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the widespread research of the surface defect detection technology based on machine vision, which has spawned various effective detection methods. In particular, the rise of deep learning has allowed the surface defect detection technology to develop further. However, these methods based on deep learning still have some drawbacks. For example, the size of the sample data is not large enough to support deep learning; the location and recognition of surface defects are not accurate enough; the real-time performance of segmentation and classification is not satisfactory. In the context, this paper proposes an end-to-end convolutional neural network model: the pixel-wise segmentation and image-wise classification network (PSIC-Net). With the innovative design of a three-stage network structure, improved loss function and a two-step training mode, PSIC-Net can accurately and quickly segment and classify surface defects with a small dataset of training data. This model was evaluated with three public datasets, and compared with the most advanced defect detection methods. All the performance metrics prove the effectiveness and advancement of PSIC-Net. [ABSTRACT FROM AUTHOR]

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection. [ABSTRACT FROM AUTHOR]