# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 4 Experimental Evaluation
### Evaluation Using Performance Measures  
The performance of the trained Mask R-CNN model was measured using two well-known measures in the deep learning field: precision and recall. Precision and recall are calculated based on true positives (TPs), false positives (FPs), and false negatives (FNs). Generally, precision and recall are determined using the number of TPs, FPs, and FNs as follows:  
\[\text{Precision}=\frac{\#TP}{\#TP+\#FP} \tag{1}\]  
\[\text{Recall}\frac{\#TP}{\#TP+\#FN} \tag{2}\]  
where # denotes the number of TPs, FPs, and FNs, respectively. In this study, precision and recall are determined for two different purposes: localization and segmentation, which are the main tasks of an instance segmentation model. For localization, a resulting mask whose overlap with a ground truth is equal to or higher than 70% is categorized as a TP, a resulting mask whose overlap with a ground truth is lower than 70% is categorized as an FP, and a ground truth whose overlap is lower than 70% with a resulting mask is categorized as an FN. For segmentation, the number of pixels in the resulting masks overlapping with the ground truths are used to determine the TPs, FPs, and FNs. The overlapping pixels are categorized as TPs, the pixels of resulting masks that do not overlap with the ground truths are categorized as FPs, and the pixels of ground truths that do not overlap with the resulting masks are categorized as FNs. Based on the definitions of TPs, FPs, and FNs, the precision and recall can also be calculated using Equations (1) and (2), which indicate the performance of the trained model in localizing and segmenting the damage. Table 1 lists two types of precision and recall calculated for the 25 test images. With regard to the localization metrics, the trained Mask R-CNN achieved an average of 90.43% precision and 90.81% recall for the entire test set. The results of damage types indicated that the trained Mask R-CNN achieved an average of 98.31% precision and 95.83% recall for cracks, 80% precision and 100% recall for efflorescence, 92.37% precision and 95.55% recall for spalling and rebar exposure, and 86.73% precision and 76.66% recall for multiple collocated damage. In segmentation metrics, the trained Mask R-CNN achieved an average of 87.24% precision and 87.58% recall for the entire test set. The results of damage types indicated that the trained Mask R-CNN achieved an average of 88.92% precision and 85.63% recall for cracks, 91.15% precision and 95.18% recall for efflorescence, 88.05% precision and 89.30% recall for spalling and rebar exposure, and 89.47% precision and 89.74% recall for multiple collocated damage. A visual investigation of the figures indicates the successful detection of multiple damages by the trained Mask R-CNN model. In Figure 5, the cracks were completely detected with minimal misses and no false alarms, although there are various nondamaged objects in the vicinity such as stains. In the multiple complex crack patterns in Figure 5b, a thin crack branch at the bottom right of the image was missed owing to the limitation of the resolution. In Figure 6, efflorescence was successfully detected, and the shapes were nearly identical to the ground truths. A false alarm was detected on a steel cantilevered support of the catwalk at the top right of Figure 6b owing to its bright gray color and complex shape. In Figure 7, severe spalling with rebar exposure was successfully detected except for one false alarm of cracks owing to the wavy pattern of the paint covering the concrete. In Figure 8, the trained model showed robust performance in detecting collocated multiple damage, while Figure 8c,d showed certain conditions that imped the accurate inference though. In Figure 8c, relatively narrow cracks around the spalling were missed, while wide cracks were successfully detected. In addition, some of the cracks in the middle of the right side of Figure 8c were missed because they had visual features similar to those of overlaid cement paste, which was trained as a noncrack object.