# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 3 Data Collection and Training Parameters  
As explained before, collecting a large enough dataset of complex cracks with diverse material preparation techniques takes years. The experiments here aim to demonstrate the applicability of the proposed methodology on complex (micro)cracks. Moreover, the demonstration of the performance of the proposed method on a more complex scenario will entail that the capability of this technique in other less complex cases is not adversely affected. With these considerations, a dataset is developed through a combination of laboratory tests and field datasets depicting damage to buildings and pavement. The details of the field dataset are discussed further. More specifically, the laboratory dataset consists of images that depict surface cracks of damaged strain-hardening cementitious composite (SHCC) samples through various accelerated damage tests performed in laboratory conditions using our prior experience. What sets SHCCs apart are high-density (up to \(\sim\)300/m), tortuous (micro)cracks with widths of \(\sim\)60 microns and the high range of these crack features (HRCF) observed in tandem [9; 52]. These values are several orders more complex. For example, cracks in typical concrete are in orders of a few millimeters (100 times higher); similarly, the crack density in the field dataset is \(\sim\)10\(\times\) lower.  
These datasets were collected sporadically over a long period of time. As a result, the imaging devices, lighting conditions, surface characteristics, and artifacts are different. The dataset consists of 1078 images taken of \(\sim\)70 different samples. The original images were then processed to compute the expected output (also known as ground truth) for training. This is described in the following section.  
Training details should also be mentioned. The total dataset was divided into training, validation, and test sets with the following ratio: 0.7, 0.1, and 0.2, respectively. During training, the images were cropped randomly into the resolution of 224 \(\times\) 224 \(\times\) 3 and trained in a batch size of 32. The Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-3}\) was employed in this study to update the weights of the network after each iteration. Training was deemed complete when for next the 10 epochs no improvement in network performance was observed. NVIDIA GPU-1080 was employed to train the neural network.  
The network was trained for 120 epochs. The following performance metrics were quantified: accuracy, IOU, and boundary F1 (BF) score. The accuracy measures how well each pixel is classified, IOU measures the similarity between the predicted cracked area and the original area, and the BF score is a measure of how well the predicted boundary matches the object (crack) with the known boundary. Equations (7) and (8) are general equations describing accuracy and BF score. The average values of these metrics are 0.88, 0.81, and 0.84, respectively, on the test set.  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]\[BF\ score=\frac{2\times\ precision\times\ recall}{precision+recall} \tag{8}\]