Kechen Song(Associate professor)


  • 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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NEU surface defect database

    NEU surface defect database

    Kechen Song and Yunhui Yan


    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.

         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


       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.

       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)



        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:

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


Oil pollution defect database

    Oil pollution defect database:

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

    Oil pollution defect database.jpg