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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  • DATABASE

Visible-Depth-Thermal Image Dataset

    A Novel Visible-Depth-Thermal Image Dataset of Salient Object Detection

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    Visual perception plays an important role in industrial information field, especially in robotic grasping application. In order to detect the object to be grasped quickly and accurately, salient object detection (SOD) is employed to the above task. Although the existing SOD methods have achieved impressive performance, they still have some limitations in the complex interference environment of practical application. To better deal with the complex interference environment, a novel triple-modal images fusion strategy is proposed to implement SOD for robotic visual perception, namely visible-depth-thermal (VDT) SOD. Meanwhile, we build an image acquisition system under variable lighting scene and construct a novel benchmark dataset for VDT SOD (VDT-2048 dataset). Multiple modal images will be introduced to assist each other to highlight the salient regions. But, inevitably, interference will also be introduced. In order to achieve effective cross-modal feature fusion while suppressing information interference, a hierarchical weighted suppress interference (HWSI) method is proposed. The comprehensive experimental results prove that our method achieves better performance than the state-of-the-art methods.  
     Kechen Song, et al.  A Novel Visible-Depth-Thermal Image Dataset of Salient Object Detection for Robotic Visual Perception [J]. IEEE/ASME Transactions on Mechatronics, 2022.  (paper) (Dataset & Code



NEU surface defect database

    NEU surface defect database

    Kechen Song and Yunhui Yan

    Description:    


    In the Northeastern University (NEU) surface defect database, six kinds of typical surface defects of the hot-rolled steel strip are collected, i.e., rolled-in scale (RS), patches (Pa), crazing (Cr), pitted surface (PS), inclusion (In) and scratches (Sc). The database includes 1,800 grayscale images: 300 samples each of six different kinds of typical surface defects.

    Fig. 1 shows the sample images of six kinds of typical surface defects, the original resolution of each image is 200×200 pixels. From Fig. 1, we can clearly observe that the intra-class defects existing large differences in appearance, for instance, the scratches (the last column) may be horizontal scratch, vertical scratch, and slanting scratch, etc. Meanwhile the inter-class defects have similar aspects, e.g., rolled-in scale, crazing, and pitted surface. In addition, due to the influence of the illumination and material changes, the grayscale of the intra-class defect images is varied. In short, the NEU surface defect database includes two difficult challenges, i.e., the intra-class defects existing large differences in appearance while the inter-class defects have similar aspects, the defect images suffer from the influence of illumination and material changes.

    NEU surface defect database.jpg

                                                                                                       Fig.1 NEU-CLS

    For defect detection task, we provided annotations which indicate the class and location of a defect in each image. We have carefully clicked annotations of each target in these images. Fig. 2 shows some examples of detection results on NEU-DET. For each defect, the yellow box is the bounding box indicating its location and the green label is the class score.

    NEU surface defect database-Fig.2.png

    Fig.2 NEU-DET

    How to use the database:   
        If you are using the NEU surface defect database for defect classification task, you only need to download the image-only file, called NEU-CLS.
        If you are using the NEU surface defect database for defect detection task, you need to download the image-with-annotations file, called NEU-DET.

    Download:  
         For Original NEU surface defect databaseGoogle.pngGoogle-Drive, OrBaidu.png Baidu-Pan
         For NEU-CLSGoogle.pngGoogle-Drive, Or Baidu.pngBaidu-Pan
         For NEU-CLS-64:Google.pngGoogle-Drive, Or Baidu.pngBaidu-Pan
         For NEU-DET:Google.pngGoogle-Drive, Or Baidu.pngBaidu-Pan(code:pmqx)

        For   SD-saliency-900 database

              NEU-Seg

    Citation:    
       We would appreciate it if you cite our works when using the database:

          Yanqi Bao, Kechen Song, Jie Liu, Yanyan Wang, Yunhui Yan, Han Yu, Xingjie Li, “Triplet- Graph Reasoning Network for Few-shot Metal Generic Surface Defect Segmentation,” IEEE Transactions on Instrumentation and Measuremente,  2021.(paper)
        K. Song and Y. Yan, “A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,” Applied Surface Science, vol. 285, pp. 858-864, Nov. 2013.
    (paper)

       Yu He, Kechen Song, Qinggang Meng, Yunhui Yan, “An End-to-end Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features,” IEEE Transactions on Instrumentation and Measuremente,  2020,69(4),1493-1504.(paper)

     

     

    Links:   
        Vision-based Inspection System for Steel Surface Defects
        For Micro surface defect database:Google.pngGoogle-Drive, OrBaidu.pngBaidu-Pan
        For Oil pollution defect database:Google.pngGoogle-Drive, Or Baidu.pngBaidu-Pan






Micro surface defect database

    Micro surface defect database:

    Download:  
              Micro surface defect database:Google.pngGoogle-Drive, OrBaidu.pngBaidu-Pan

    SCACM.jpg



Oil pollution defect database

    Oil pollution defect database:

    Download:  
              Oil pollution defect database:Google.pngGoogle-Drive, Or Baidu.pngBaidu-Pan 

    Oil pollution defect database.jpg