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Eval

Evaluation tools for segmentation and edge/centerline detection.

Evaluation

  • Segmentation Metrics:

    • Global Accuracy (G)
    • Class Average Accuracy (C)
    • Mean IOU (I/U)

  • Sensitivity and Specificity Metrics:

    • Precision (P)
    • Recall (R)
    • F-score (F)

Crack Detection:

Released results:

Note: The PyTorch implementation with the same loss achieves lower performances than the Caffe implementation. So, we suggest to set the loss mode as focal in the configuration file train_deepcrack.sh.

Outputs bT G C I/U P R F
DeepCrack-BN 0.31 0.9873 0.9196 0.8643 0.8582 0.8456 0.8518
DeepCrack-GF 0.48 0.9888 0.9261 0.8778 0.8795 0.8575 0.8684
Side-output 1 0.43 0.9836 0.8930 0.8298 0.8208 0.7939 0.8071
Side-output 2 0.42 0.9863 0.9093 0.8543 0.8537 0.8250 0.8391
Side-output 3 0.36 0.9854 0.9110 0.8482 0.8334 0.8295 0.8315
Side-output 4 0.36 0.9823 0.8989 0.8228 0.7886 0.8077 0.7980
Side-output 5 0.38 0.9735 0.8814 0.7663 0.6646 0.7807 0.7180

For comparisons, you can download our predicted images and evaluation files from google drive:

deepcrack
  |__ evaluation
  |     |__ ...
  |__ test_latest
        |__images
             |__ ...
  • *_image.png: input images,
  • *_label_viz.png: ground truth,
  • *_fused.png: outputs of fused layer,
  • *_gf.png: refined predictions by guided filter, see the code tools/guided_filter.py,
  • *_side1.png: side output 1,
  • *_side2.png: side output 2,
  • *_side3.png: side output 3,
  • *_side4.png: side output 4,
  • *_side5.png: side output 5,

TODO: CRF refinement module will be released soon...