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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
  • E-Mail:
  • 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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  • Rail Surface Defect Detection

In-service and No-service Rail 

Rail surface defect inspection.png


No-service Rail Surface Defect Detection based on Stereoscopic Images (RGB-D)

Unsupervised Saliency Detection

Unsupervised.jpg

An unsupervised stereoscopic saliency detection method based on a binocular line-scanning system is proposed in this article. This method can simultaneously obtain a highly precise image as well as profile information while also avoids the decoding distortion of the structured light reconstruction method. 
Menghui Niu, Kechen Song, et al.  Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images [J]. IEEE Transactions on Industrial Informatics, 2021,17(3),2271-2281.  (paper) (code)(RSDDS-113 dataset) (ESI highly cited, 7/2021-1/2023)HighlyCitedPaper.png  Reported by 《Imaging & Machine Vision Europe


Collaborative Learning Attention Network


We propose a neural network named collaborative learning attention network (CLANet) for no-service rail surface defect inspection. The proposed method consists of three main stages: feature extraction, cross-modal information fusion, and defect location and segmentation. A multimodal attention block is proposed to highlight complex defect object with a new cross-modal fusion strategy. Furthermore, dual stream decoder enriches the representation of advanced features and avoids the dilution of information in the decoding stage. Suffering from the scarcity of defective data, an industrial RGB-D dataset NEU RSDDS-AUG is built. Finally, ablation studies verify the effectiveness of our proposed method.

2022-Collaborative Learning Attention Network Based on RGB Image and Depth Image for Surface Defect Inspection of No-Service Rail.jpg

Jingpeng Wang, Kechen Song, et al. Collaborative Learning Attention Network Based on RGB Image and Depth Image for Surface Defect Inspection of No-Service Rail [J]. IEEE/ASME Transactions on Mechatronics, 2022 . (paper)



No-service Rail Surface Defect Detection based on RGB Images

MCnet

MCnet.jpg

In this article, we propose an acquisition scheme with two lamp light and color scan line charge-coupled device (CCD) to alleviate uneven illumination. Then, a multiple context information segmentation network is proposed to improve NRSD segmentation. The network makes full use of context information based on dense block, pyramid pooling module, and multi-information integration. Besides, the attention mechanism is applied to optimize extracted information by filtering noise. For the problem of real sample shortage, we propose to utilize artificial samples to train the network. And an NRSD data set NRSD-MN is built with artificial NRSDs and natural NRSDs. Experimental results show that our method is feasible and has a good segmentation effect on artificial and natural NRSDs. 
Defu Zhang, Kechen Song, et al.  MCnet: Multiple Context Information Segmentation Network of No-service Rail Surface Defects [J]. IEEE Transactions on Instrumentation and Measuremente, 2021, 70,5004309  (paper) (code) (dataset)


Image-level weakly supervised segmentation


A novel image-level weakly supervised segmentation formulation is proposed for no-service rail surface defects. These defects are decomposed into three sub-categories (strip-shaped, spot-shaped, block-shaped) according to the size prior information (area and shape). Then, a method is presented with a pooling combination module. The pooling combination module makes full use of the size attributes of the sub-category by utilizing different pooling functions for different sub-categories. Experimental results demonstrate that our method is effective and outperforms the state-of-the-art methods.

Image-level weakly supervised segmentation.jpg

Defu Zhang, Kechen Song, et al. An image-level weakly supervised segmentation method for No-service rail surface defect with size prior [J]. Mechanical Systems and Signal Processing, 2022, 165, 108334. (paper) (code)


In-service Rail Surface Defect Detection

Line-Level Label

Line-Level Label.png

A novel inspection scheme for RSDs is presented for limited samples with a line-level label, which regards defect images as sequence data and classifies pixel lines. Thousands of pixel lines are easy to be collected and labeling line-level is a simple task in labeling works. Then two methods OC-IAN and OC-TD are designed for inspecting express rail defects and common/heavy rail defects, respectively. OC-IAN and OC-TD both employ one-dimensional convolutional neural network (ODCNN) to extract features and long- and short-term memory (LSTM) network to extract context information. The main differences between OC-IAN and OC-TD are that OC-TD applies a double-branch structure and removes the attention module.

Defu Zhang, Kechen Song, et al. Two Deep Learning Networks for Rail Surface Defect Inspection of Limited Samples with Line-Level Label [J]. IEEE Transactions on Industrial Informatics, 2021,17(10),6731-6741.  (paper)




In-service and No-service Rail Surface Defect Detection

SC-OSDA
We propose a novel one-shot unsupervised domain adaptation framework. Specifically, we introduce a shape consistent style transfer module that performs pixel-level distribution alignment between the training and test images. Based on the one-shot test image, the training image is reconstructed to have the same appearance as the test image. Meanwhile, we employ a multi-task learning strategy to prevent content distortion of the reconstructed images. To improve the robustness of the model to distribution differences, we design an edge-aware defect segmentation model and train the model using the reconstructed training images. The experimental results show that our method effectively improves the robustness of the model to distribution differences and achieves satisfying results in the task of rail surface defect segmentation. SC-OSDA.jpg
Shuai Ma, Kechen Song, et al. Shape Consistent One-Shot Unsupervised Domain Adaptation for Rail Surface Defect Segmentation [J]. IEEE Transactions on Industrial Informatics, 2022 .  (paper 


CFDANet
CFDANet.jpg We propose a cross-scale fusion and domain adversarial network (CFDANet) to improve the generalization ability of deep neural networks on unseen datasets. To alleviate the domain shift caused by defect scale differences, we design a dual-encoder to extract multi-scale features from images of different resolutions. Then, those features are adaptively fused through a cross-scale fusion module. For the domain shift caused by inconsistent rail appearance, we introduce transferable-aware domain adversarial learning to extract domain invariant features from different datasets. Moreover, we further propose a transferable curriculum to suppress the negative impact of images with low transferability. Experimental results show that our CFDANet can accurately segment defects in unseen datasets and surpass other state-of-the-art domain generalization methods in all five target domain settings.

Shuai Ma, Kechen Song, et al. Cross-scale Fusion and Domain Adversarial Network for Generalizable Rail Surface Defect Segmentation on Unseen Datasets [J]. Journal of Intelligent Manufacturing, 2022 . (paper)



Anomaly Detection
An innovative generative adversarial network based on adaptive pyramid graph (APG) and variation residuals (APGVR-GAN) is proposed, aiming to improve the robustness of anomaly detection in railway products and other complex industrial supplies. First, the APG module is embedded in the encoder–decoder–encoder pattern, capturing the correlation description between neighbor regions, which is utilized to enhance the detection of abnormal defects with weak texture. Next, the variation residual module is employed to enhance the expression of various normal samples in the latent space and improve the identification ability for abnormal samples. Then, the dual-probability prototype loss is proposed to make different normal samples have more concentrated expression and more similar probability distribution centers in latent space. Finally, an adaptive focal-gate loss and a regularized log-likelihood loss are designed to overcome the imbalance problem in training samples with different background information. The effectiveness of the model is verified on three new railway datasets and three other industrial public datasets. 

APGVR-GAN.png


Menghui Niu, Kechen Song, et al. An Adaptive Pyramid Graph and Variation Residual-Based Anomaly Detection Network for Rail Surface Defects [J]. IEEE Transactions on Instrumentation and Measuremente, 2021,70,5020013 . (paper)