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10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

作者： Youkachen, Sanyapong Ruchanurucks, Miti Phatrapornnant, Teera Kaneko, Hirohiko Kasetsart Univ Fac Engn Dept Elect Engn Bangkok Thailand Natl Sci & Technol Dev Agcy Natl Elect & Comp Technol Ctr Pathum Thani Thailand Tokyo Inst Technol Dept Informat & Commun Engn Tokyo Japan

ISBN: (纸本)9781728119212

defects on steel strip surface can long-term cause undesirable effects, since they make physical and/or chemical properties mismatched from steel's specification. Nowadays, automatic visual-based surface inspection is adopted, in order to detect the defects on steel strip surface after being produced. Moreover, since these defects appear in wide variety of forms and various classes, machine learning methods are generally involved to visual surface inspection for coping with these appearance. In this paper, we present a novel defect detection model to perform defect segmentation of hot-rolled steel strip surface, by using Convolutional Auto-Encoder (CAE) and sharpening process to extract the defect features of input image, then applied post-processing for visualization. In the experiments, the NEU database, which provides six kinds of typical surface defects of hot-rolled steel strip, was applied to evaluate the efficiency of the proposed model. This database also provides difficulty challenges regarding diversity of intra-class and similarity of inter-class. The results show that the proposed model can perform defect segmentation in all kinds of defects in database, however the efficiency was compromised by illumination changes Notable that, this segmentation is based on unsupervised learning with small training dataset and no labeling procedure, so it can be easily extended to the real world application. Eventually, this defect detection shall improve the productivity and reliability of steel strip's production process.

关键词： Steel Surface Inspection defect Detection defect segmentation Unsupervised Learning Convolutional Auto encoder

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A Global-Local Feature Multilevel Fusion Network for Chip defect segmentation

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IEEE ACCESS 2024年 12卷 17467-17480页

作者： Li, Fu Shi, Linfeng Li, Yan Zhu, Xi Chen, Juan Zhu, Linglong Wuxi Univ Nanjing Univ Informat Technol Binjiang Coll Dept Internet Things Wuxi 214105 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 211544AC Jiangsu Peoples R China

In the chip encapsulation process, internal defects arise due to both natural and anthropogenic factors, primarily manifesting as a diverse array of missing Gold Wires and Solder Joints. Accurate delineation of these Gold Wires and Solder Joints through a mask is essential for defect analysis. Notably, Gold Wires exhibit a large spatial scale, while Solder Joints have a smaller scale;both possess intricate textures. Although Transformer methods can effectively model relationships between distant pixels, they tend to lose fine-grained texture information. Conversely, CNN methods excel in learning local information but may compromise spatial relationships between targets. Consequently, utilizing either of these methods alone presents limitations. In addressing this issue, we propose the Global-Local Feature Multilevel Fusion Network architecture-SegCAF, which combines the strengths of CNN and Transformer. Our research introduces a dual-branch interactive parallel network, where the CNN branch extracts local information, and the Transformer branch captures global dependencies. Subsequently, a feature fusion module aligns and aggregates the encoded features from both branches, leveraging both global context and local details. Furthermore, an auxiliary segmentation head is added to the Transformer branch to enhance training efficiency and reinforce information interaction between the two branches. In the experimental phase, we collected and annotated a dataset of X-ray images depicting internal defects in industrially produced chipsets. We conducted feature visualization analysis and model performance evaluations on this dataset. The results demonstrate that the proposed SegCAF model significantly outperforms existing mainstream models, achieving an mIoU of 90.56% and an mPA of 81.17% . This indicates that SegCAF effectively addresses the challenge of precise segmentation of chip defects using both local and global information in network design and optimization. Conseque

关键词： Enter key words or phrases in alphabetical convolutional neural network transformer multi-level interactive two-branch network defect segmentation X-ray image

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Deep Learning Based Classification of Military Cartridge Cases and defect segmentation

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IEEE ACCESS 2022年 10卷 74961-74976页

作者： Tural, Serhat Samet, Refik Aydin, Semra Traore, Mohamed Ankara Univ Dept Comp Engn TR-06100 Ankara Turkey Natl Def Univ Dept Comp Engn TR-06420 Ankara Turkey

The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the non-uniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem has been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective, and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented, and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100%

关键词： Surface treatment Deep learning Image segmentation Surface morphology Surface texture Quality control Object segmentation Deep learning defect detection defect segmentation defect texture object inspection military cartridge case object classification non-uniform surface texture quality control

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Semisupervised defect segmentation With Pairwise Similarity Map Consistency and Ensemble-Based Cross Pseudolabels

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第9期19卷 9535-9545页

作者： Sime, Dejene M. Wang, Guotai Zeng, Zhi Wang, Wei Peng, Bei Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China

Deep-learning-based automatic defect segmentation is one of the hot research areas in computer vision application for the task of intelligent industrial inspection. Recently, several state-of-the-art models for image segmentation task have been proposed. However, their high performance is vastly dependent on the availability of large set of labeled data, which is one of the hindering factors in achieving full potential with deep learning methods in industrial inspection. In this article, we propose a novel method based on pairwise similarity map consistency with ensemble-based cross pseudolabels for semisupervised defect segmentation that uses limited labeled samples while exploiting additional label-free samples. The proposed approach uses three network branches that are regularized by pairwise similarity map consistency, and each of them is supervised by the pseudolabels generated by ensemble of predictions of the other two networks for the unlabeled samples. The proposed method achieved significant performance improvement over the baseline of learning only from the labeled images and the current state-of-the-art semisupervised methods. We perform ablation studies and extensive experiments on various parameters and components to demonstrate that our method achieved state-of-the-art results on three different datasets.

关键词： Consistency regularization defect segmentation pairwise similarity pseudolabels semisupervised learning

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SIIF: Semantic information interactive fusion network for photovoltaic defect segmentation

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APPLIED ENERGY 2024年 371卷

作者： Zhou, Peng Wang, Rui Wang, Chuhan Chen, Haiyong Liu, Kun Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin 300130 Peoples R China

Any defects or damage on photovoltaic panels significantly reduce photoelectric conversion efficiency and service life. Therefore, the refined defect segmentation technology is the key to the quality inspection of photovoltaic equipment. However, the existing methods ignore the information interaction between multilevels, causing semantic features to be easily lost and difficult to fuse. In this paper, we propose a segmentation network based on an interactive fusion of semantic information for solar cell defect identification, which can effectively interact with multi-level rich information and enhance the positioning capabilities of high-level semantics. Specifically, we propose a novel Multi-Level Pyramid Strategy (MLPS) to reduce the information gap between multi-level features. MLPS adopts a pooling distribution mechanism to enable multi-level features to interact effectively in the feature pyramid. In addition, to effectively integrate high-level positioning semantics into the neighbor level, we design a new Neighbor Attention Fusion module (NAF) to propagate high-level semantic information gradually. NAF learns spatial neighbor-level similarity combined with highlevel attention to balance location information and contextual features. We perform comprehensive experiments to demonstrate the advancement of our approach. Our method achieves 84.3% MIOU on our solar cell dataset, outperforming existing state-of-the-art (SOTA) methods. To verify the generalization ability of our network, we train and evaluate the PSCDE dataset. The results show that our model achieves an excellent 98.35%MIOU.

关键词： defect segmentation Photovoltaic defect detection Semantic interaction Attention fusion

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Sheared edge defect segmentation using a convolutional U-Net for quantified quality assessment of fine blanked workpieces

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PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2022年 75卷 129-141页

作者： Wolfschlager, Dominik Woltersmann, Jan-Henrik Montavon, Benjamin Schmitt, Robert H. Rhein Westfal TH Aachen Chair Prod Metrol & Qual Management WZL Campus Blvd 30 D-52074 Aachen Germany Fraunhofer Inst Prod Technol IPT Aachen Germany

Fine blanking allows for the economic mass production of sheet metal parts of high dimensional accuracy. The smooth cut section at the sheared edge is an important quality characteristic that is, however, reduced by the formation of tearings and fractures in the cut-off zone. At present, the smooth cut section cannot be assessed immediately as no capable methods for a 100% inline quality control of sheared edges for fine blanking exist, causing high scrap rates. To address this deficit this work proposes U-Nets to segment tearings and measure the cut-off height at sheared edges. A systematic model search together with a transfer learning strategy is conducted based on a dataset of 80 images of sheared edges of fine blanked reference workpieces. After a hyperparameter optimization, trained models are able to measure the cut-off height on the test dataset with a mean relative error of 1.1% and a mean absolute error of < 1 px corresponding to an average measurement error of 7.5 mu m. Besides the natural ability to incorporate additional disturbances in a real-world application, investigations of the reproducibility and the impact of the dataset size indicate that the proposed U-Net model is easy to set up and achieves a comparable average performance of 1.14 & PLUSMN;0.31 px (17.1 & PLUSMN;3.7 mu m) even for non-optimal model initialization and small training datasets. For this reason, the shown approach is a promising solution for an inline machine vision system.

关键词： Fine blanking Deep learning Quality assurance defect segmentation Machine vision

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Progressive Complementary Knowledge Aggregation for CdZnTe defect segmentation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9870-9880页

作者： Li, Peihao Li, Feng Liu, Man Bai, Huihui Wei, Yunchao Wang, Anhong Ma, Shijie Zhao, Yao Beijing Jiaotong Univ Beijing Key Lab Adv Informat Sci & Network Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Sch Artificial Intelligence Hefei 230601 Peoples R China Taiyuan Univ Sci & Technol Inst Digital Media Commun Taiyuan 030024 Peoples R China China Elect Technol Grp Corp Res Inst 2 Beijing 100846 Peoples R China

Automatic quality inspection of industrial products is an indispensable part of modern manufacturing. Cadmium zinc telluride (CdZnTe) crystal is an important industrial raw material, but the special photosensitive properties of CdZnTe make it show different defect boundaries under different lighting angles, which poses challenges for quality inspection. In this article, we propose progressive complementary knowledge aggregation (PCKA) for CdZnTe defect segmentation, which is model-agnostic. First, the 12 images of CdZnTe crystal with different lighting angles are fed into the preliminary aggregation net to aggregate unique pixel-level clues. Second, we use a latent aggregation net to acquire the feature-level complementary clues under the guidance of the pixel-level clues within latent space. Such a learning paradigm is an effective solution for the special photosensitive properties of CdZnTe crystal. Extensive experiments on self-collected dataset demonstrate the effectiveness and efficiency of our PCKA compared with other solutions.

关键词： Computer vision (CV) deep learning defect segmentation

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Latent Diffusion Models to Enhance the Performance of Visual defect segmentation Networks in Steel Surface Inspection

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SENSORS 2024年第18期24卷 6016页

作者： Leinena, Jon Saiz, Fatima A. Barandiaran, Inigo Basque Res & Technol Alliance BRTA Vicomtech Fdn Donostia San Sebastian 20009 Spain

This paper explores the use of state-of-the-art latent diffusion models, specifically stable diffusion, to generate synthetic images for improving the robustness of visual defect segmentation in manufacturing components. Given the scarcity and imbalance of real-world defect data, synthetic data generation offers a promising solution for training deep learning models. We fine-tuned stable diffusion using the LoRA technique on the NEU-seg dataset and evaluated the impact of different ratios of synthetic to real images on the training set of DeepLabV3+ and FPN segmentation models. Our results demonstrated a significant improvement in mean Intersection over Union (mIoU) when the training dataset was augmented with synthetic images. This study highlights the potential of diffusion models for enhancing the quality and diversity of training data in industrial defect detection, leading to more accurate and reliable segmentation results. The proposed approach achieved improvements of 5.95% and 6.85% in mIoU of defect segmentation on each model over the original dataset.

关键词： defect segmentation data augmentation stable diffusion industrial manufacturing quality control

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Real-time and accurate defect segmentation of aluminum strip surface via a lightweight network

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2023年第2期20卷 37页

作者： Lv, Zehua Li, Yibo Qian, Siying Cent South Univ Light Alloy Res Inst Changsha 410083 Peoples R China

On the premise of ensuring the defect segmentation precision of aluminum strip surfaces, improving the segmentation speed to meet the real-time requirements of the production line is an important task. Therefore, a lightweight and efficient network is proposed for the defect segmentation of aluminum strip surfaces. In the network, the lightweight GhostNet with the proposed dilation attention mechanism embedded is used for multi-scale feature extraction. This mechanism focuses more on the critical space and channel features and obtains a large receptive field in the spatial dimension. The Ghost module-based lightweight fusion node is constructed and embedded into the bidirectional feature pyramid network (BiFPN) for more efficient integration of multi-scale features. In addition, a novel lightweight boundary refinement (LBR) block designed as a residual structure is suggested to improve the localization ability near the defect boundaries. The aluminum strip surface dataset with five kinds of common defects is created and adopted to train and test the networks. The evaluation results demonstrate that the mean intersection over union (mIoU) of the proposed network is 85.51%, the speed is 68.86 fps, and the model volume is 9.38 MB. In summary, the proposed network gets a good trade-off between defect segmentation speed and accuracy for aluminum strip surfaces, which provides the potential for real-time segmentation on embedded systems.

关键词： defect segmentation Aluminum strip surface Lightweight framework Attention mechanism

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Automatic defect segmentation in X-Ray Images Based on Deep Learning

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第12期68卷 12912-12920页

作者： Du, Wangzhe Shen, Hongyao Fu, Jianzhong Zhejiang Univ Coll Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Coll Mech Engn Key Lab 3D Printing Proc & Equipment Zhejiang Pr Hangzhou 310027 Peoples R China

X-ray imaging has been broadly adopted as a nondestructive testing method for product quality inspection. Deep learning has demonstrated powerful image scene understanding capabilities. In this article, U-Net with resnet101 is taken as the baseline for defect segmentation. First, there exist gray inhomogeneous and low-contrast regions in X-ray images, which can hardly be segmented. Contrast-limited adaptive histogram equalization (CLAHE) could be used to improve the contrast and consistency of the X-ray image. A two-stream convolutional neural network (CNN) is proposed that takes the original image and CLAHE processed image as inputs to address this issue. And then in CNN, low-level feature maps are lacking semantic information, which may lead to worse results. A gated multilayer fusion module is proposed to adaptively fuse the high-level features into low-level features. Furthermore, loss functions (such as cross entropy) in semantic segmentation are usually pixel level, ignoring the regional information. A weighted intersection over union (IOU) loss function is proposed to introduce IOU information to guide the model to focus on the objects that are easy to mine. The experimental results prove that the three proposed methods have better performance than the baseline for our dataset, achieving 42.2 in mIoU, 59.2 in Dice, and 54.5%, 74.9%, and 86.3% in small, middle, and large object recall rate, respectively.

关键词： Image segmentation X-ray imaging Feature extraction Semantics Deep learning Object segmentation Manufacturing Casting parts computer vision deep learning defect segmentation nondestructive testing (NDT) X-ray image

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