README.md

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...