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Kechen Song(Associate professor)

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  • Supervisor of Doctorate Candidates  Supervisor of Master's Candidates
  • Name (English):Kechen Song
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  • Education Level:With Certificate of Graduation for Doctorate Study
  • Gender:Male
  • Degree:博士
  • Status:Employed
  • Alma Mater:东北大学
  • Teacher College:机械工程与自动化学院

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  • Strip Steel Surface Defect Detection

Surface Defect  Segmentation and Classification based on Few-shot Learning

Segmentation-CPANet
This paper proposed a simple but effective few-shot segmentation method named cross position aggregation network (CPANet), which intends to learn a network that can segment untrained S3D categories with only a few labeled defective samples. Using a cross-position proxy (CPP) module, our CPANet can effectively aggregate long-range relationships of discrete defects, and support auxiliary (SA) can further improve the feature aggregation capability of CPP. Moreover, CPANet introduces a space-squeeze attention (SSA) module to aggregate multi-scale context information of defect features and suppresses disadvantageous interference from background information. In addition, a novel S3D few-shot semantic segmentation dataset FSSD-12 is proposed to evaluate our CPANet. Through extensive comparison experiments and ablation experiments, we explicitly evaluate that our CPANet with the ResNet-50 backbone achieves state-of-the-art performance on dataset FSSD-12. CPANet.png
Hu Feng, Kechen Song, et al. Cross Position Aggregation Network for Few-shot Strip Steel Surface Defect Segmentation [J]. IEEE Transactions on Instrumentation and Measuremente, 2023, 72, 5007410. (paper) (code & dataset)


Segmentation-TGRNet
TGRNet.jpg Metal surface defect segmentation can play an important role in dealing with the issue of quality control during the production and manufacturing stages. There are still two major challenges in industrial applications. One is the case that the number of metal surface defect samples is severely insufficient, and the other is that the most existing algorithms can only be used for a specific surface defects and it is difficult to generalize to other metal surfaces. In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph Reasoning Network (TGRNet) and a novel dataset Surface Defects-4i are proposed to achieve this theory. For Surface Defects-4i, it includes multiple categories of metal surface defect images to verify the generalization performance of our TGRNet and adds the non-metal categories (leather and tile) as extensions.
Yanqi Bao, Kechen Song, et al. Triplet- Graph Reasoning Network for Few-shot Metal Generic Surface Defect Segmentation [J]. IEEE Transactions on Instrumentation and Measuremente, 2021. (paper) (code & dataset)(ESI highly cited, 5/2022)HighlyCitedPaper.png


Classification-GTNet
In this article, we propose a novel few-shot defect classification method, which aims to recognize novel defective classes with few labeled samples. Specifically, the proposed method follows a transductive paradigm and consists of two modules, i.e., graph embedding and distribution transformation (GEDT) module and optimal transport (OPT) module. The GEDT module not only makes full use of the relevant correlation information between different features in the support set and the query set but also ensures the consistent distribution of the graph embedding results. Then, the OPT module is leveraged to implement few-shot classification in a transductive manner. Finally, experiments conducted on the proposed metal surface defect dataset, and the results demonstrate that the proposed method achieves the state-of-the-art performance under both one-shot and five-shot settings. GTnet.jpg
Weiwei Xiao, Kechen Song, et al. Graph Embedding and Optimal Transport for Few-Shot Classification of Metal Surface Defect [J]. IEEE Transactions on Instrumentation and Measuremente, 2022. (paper) (code & dataset)


Classification-FaNet
FaNet.png

In this paper, we propose a feature-aware network (FaNet) for a few shot defect classification, which can effectively distinguish new classes with a small number of labeled samples. In our proposed FaNet, we use ResNet12 as our baseline. The feature-attention convolution module (FAC) is applied to extract the comprehensive feature information from the base classes, as well as to fuse semantic information by capturing the long-range feature relationships between the upper and lower layers. Meanwhile, during the test phase, an online feature-enhance integration module (FEI) is adopted to average the noise from the support set and query set defect images, further enhancing image features among the different tasks. In addition, we construct a large-scale strip steel surface defects few shot classification dataset (FSC-20) with 20 different types. Experimental results show that the proposed method achieves the best performance compared to state-of-the-art methods for the 5-way 1-shot and 5-way 5-shot tasks. 

Wenli Zhao, Kechen Song, et al. FaNet: Feature-aware Network for Few Shot Classification of Strip Steel Surface Defects [J]. Measurement, 2023. (paper) (code & dataset)


Surface Defect Detection, Segmentation and Classification based on Supervised/ Semi-supervised

Detection

NEU surface defect database-Fig.2.png

In this paper, we proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection. In order to achieve strong classification-ability, this system employs a baseline convolution neural network (CNN) to generate feature maps at each stage. And then the proposed multilevel-feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects. Based on these multilevel features, a region proposal network (RPN) is adopted to generate regions of interest (ROIs). For each ROI, a detector, consisting of a classifier and a bounding box regressor, produces the final detection results. Finally, we set up a defect detection dataset NEU-DET for training and evaluating our method. On the NEU-DET, our method achieves 74.8/82.3 mAP with baseline networks ResNet34/50 by using 300 proposals. In addition, by using only 50 proposals, our method can detect at 20 fps on a single GPU and reach 92% of the above performance, hence the potential for real-time detection.
Yu He, Kechen Song, et al. An End-to-end Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features[J]. IEEE Transactions on Instrumentation and Measuremente, 2020. (paper) (dataset) (Popular Articles, 12/2020--1/2023) (ESI highly cited, 12/2020-1/2023)HighlyCitedPaper.png(ESI Hot Paper,5/2022)ESI Hot Paper.png


Segmentation

PGA-Net.jpg


This article proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multiscale features are extracted at first from backbone network. Then the pyramid feature fusion module is used to fuse these features into five resolutions through some efficient dense skip connections. Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine the boundary of defect and improve the result of the prediction. The final prediction is the fusion of the five resolutions fusion feature maps. The results of evaluation on four real-world defect datasets demonstrate that the proposed method outperforms the state-of-the-art methods on mean intersection of union and mean pixel accuracy (NEU-Seg: 82.15%, DAGM 2007: 74.78%, MT_defect: 71.31%, Road_defect: 79.54%).


Hongwen Dong, Kechen Song, et al. PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection [J]. IEEE Transactions on Industrial Informatics, 2020,16(12),7448-7458. (paper) (dataset)(ESI highly cited, 7/2021--1/2023)HighlyCitedPaper.png


Classification of Semi-supervised
Defect inspection is very important for guaranteeing the surface quality of industrial steel products, but related methods are based primarily on supervised learning which requires ample labeled samples for training. However, there can be no doubt that inspecting defects on steel surface is always a data-limited task due to difficult sample collection and expensive expert labeling. Unlike the previous works in which only labeled samples are treated using supervised classifiers, we propose a semi-supervised learning (SSL) defect classification approach based on multi-training of two different networks: a categorized generative adversarial network (GAN) and a residual network. This method uses the GAN to generate a large number of unlabeled samples. And then the multi-training algorithm that uses two classifiers based on different learning strategies is proposed to integrate both labeled and unlabeled into SSL process. Finally, through the multiple training process, our SSL method can acquire higher accuracy and better robustness than the supervised one using only limited labeled samples. Experimental results clearly demonstrate that the effectiveness of our proposed method, achieving the classification accuracy of 99.56%.

SSL.jpg

Yu He, Kechen Song, et al. Semi-supervised Defect Classification of Steel Surface Based on Multi-training and Generative Adversarial Network [J]. Optics and Lasers in Engineering, 2019, 122: 294-302. (paper




Surface Defect Feature Extraction and Recognition based on Traditional Methods


Adjacent Evaluation Completed Local Binary Patterns (AECLBP):
NEU surface defect database.jpg Automatic recognition method for hot-rolled steel strip surface defects is important to the steel surface inspection system. In order to improve the recognition rate, a new, simple, yet robust feature descriptor against noise named the adjacent evaluation completed local binary patterns (AECLBP) is proposed for defect recognition. In the proposed approach, an adjacent evaluation window which is around the neighbor is constructed to modify the threshold scheme of the completed local binary pattern (CLBP). Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes. Even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy. In addition, the strategy of using adjacent evaluation window can also be used in other methods of local binary pattern (LBP) variants.

Kechen Song and Yunhui Yan. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects [J].Applied Surface Science, 2013, 285: 858-864. (database)



Scattering Convolution Network(SCN):
In order to improve the tolerance ability of local deformations for current feature extraction methods, a scattering operator is applied to extract features for defect recognition. Firstly, a scattering transform builds non-linear invariants representation by cascading wavelet transforms and modulus pooling operators, which average the amplitude of iterated wavelet coefficients. Then, an improved network named the scattering convolution network (SCN) is introduced to build largescale invariants. Finally, a surface defect database named the Northeastern University (NEU) surface defect database is constructed to evaluate the effectiveness of the feature extraction methods for defect recognition. Experimental results demonstrate that the SCN method presents the excellent performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes. Even in the less number of training, the SCN method can still achieve the moderate recognition accuracy.

NEU-Scatt.png


Kechen Song, Shaopeng Hu and Yunhui Yan. Automatic Recognition of Surface Defects on Hot-Rolled Steel Strip Using Scattering Convolution Network [J].Journal of Computational Information Systems, 2014, 10(7):3049-3055 . (paper)


Surface Defect Detection based on Traditional Methods

Saliency Linear Scanning Morphology(SLSM):
SLSM.jpg Surface defect detection of silicon steel strip is an important section for non-destructive testing system in iron and steel industry. To detect the interesting defect objects for silicon steel strip under oil pollution interference, a new detection method based on saliency linear scanning morphology is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background. Meanwhile, a saliency map is obtained for the purpose of highlighting the potential objects. Then, the linear scanning operation is proposed to obtain the region of oil pollution. Finally, the morphology edge processing is proposed to remove the edge of oil pollution interference and the edge of reflective pseudo-defect. Experimental results demonstrate that the proposed method presents the well performance for detecting surface defects including wipe-crack-defect, scratch-defect and small-defect.

Kechen Song, Shaopeng Hu, Yunhui Yan and Jun Li. Surface defect detection method using saliency linear scanning morphology for silicon steel strip under oil pollution interference[J]. ISIJ International, 2014, 54(11):2598-2607 .



Saliency Convex Active Contour Model(SCACM):
Accurate detection of surface defects is an indispensable section in steel surface inspection system. In order to detect the micro surface defect of silicon steel strip, a new detection method based on the saliency convex active contour model is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background for the purpose of highlighting the potential objects. The extracted saliency map is then exploited as a feature, which is fused into a convex energy minimization function of local-based active contour. Meanwhile, a numerical minimization algorithm is introduced to separate the micro surface defects from cluttered background. Experimental results demonstrate that the proposed method presents the well performance for detecting micro surface defects including spot-defect and steel-pit-defect. Even in the cluttered background, the proposed method almost detects all of the micro defects without any false objects.

SCACM.jpg


Kechen Song and Yunhui Yan. Micro surface defect detection method for silicon steel strip based on saliency convex active contour model[J].Mathematical Problems in Engineering, 2013,  (paper)




Surface Defect Image Segmentation based on Traditional Methods

Convex Active Contour Segmentation Model:

Convex_Active_Contour_Segmentation_Model.jpg

In order to solve problems existing in Chan-Vese model and Local Binary Fitting (LBF) model, such as model sensitivity to the initial contour position and running slow in the segmentation of strip steel defect image, a novel model local information-based convex active contour (LICAC) is proposed. By converting non-convex optimization problem to a convex optimization problem via convex optimization technology , and applying the Split Bregman method for fast solutionthe issues of the sensitivity to the initial contour position occurring in Chan-Vese model and LBF model are solved. With introduction of the local information, the new model is efficient in the segmentation of the strip surface defect image which is non-uniform gray. By using this model to segment single-target region strip defect image, four common defect categories, including weld, rust, holes and scratches are experimented, and experimental results show that the segmentation effect and operation time of the proposed model are better than the rest two kinds. In addition, this model can also be used to segment multi-target regions defect image, four common defect categories are experimented, including scratches, inclusion, pitting, and wrinkles, and experimental results have verified the validity of the model.

SONG Kechen, YAN Yunhui, PENG Yishu, DONG Dewei. Convex Active Contour Segmentation Model of Strip Steel Defects Image Based on Local Information[J].JOURNAL OF MECHANICAL ENGINEERING,2012,48(20):1-7. (Chinese)



Structure Tensor and Active Contour:
In order to address the segmentation problem for cold rolled silicon steel surface defect based on the texture background, a novel method based on structure tensor and active contour model is proposed. Firstly, image local information is introduced to the structure tensor. In the extracted feature space of structure tensor, KL distance is treated as a regional similarity measure of the probability density to establish active contour model for image segmentation. Finally the numerical solution of Split-Bregman is used to solve the model. The proposed method is introduced to segment silicon steel surface defects, which are longitudinal scratches, horizontal scratches, foreign bodies, and holes. The experimental results show that this method can segment the silicon steel surface defect areas accurately.

Structure_Tensor_Active_Contour.jpg


SONG Kechen, YAN Yunhui,WANG Zhan, HU Changfa. Research on segmentation method for silicon steel surface defect based on structure tensor and active contour[J].Computer Engineering and Applications,2012,48(32):224-228.(Chinese)