# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 4 Results and Discussion
### Performance Evaluation  
Table 1 compares the damage detectability of all models considering precision, recall, F1 score, and IoU according to damage type. DeepLabV3+ with the ResNet50 backbone could be considered a benchmark for all other models because it achieved the best performance for all types of damage, as expected. Consequently, the performances of the other models are comparable. Figure 10: Example of prediction results of all models in complex background conditions. four models (ENet, CGNet, ESNet, and DDRNet-23-Slim) were compared regarding the ratio of F1 scores and IoUs, using DeepLabV3+ (ResNet50) as the benchmark. The maximum values of precision and recall were inconsistently achieved by different models in each damage class; however, the F1 score could be a balanced metric. Regarding the cracks, there was a significant difference between the precision and recall values of all models. A lower precision indicated a higher number of FPs (i.e., false alarms) in the prediction of cracks. Cracks usually had very narrow widths, and, in most cases, the cracks were predicted to be wider than the ground truths, which significantly lowered precision. ESNet achieved the best F1 score of 0.64 and IoU of 0.46, compared with the other models. The detectability ratio of ESNet over DeepLabV3+ was 91.6% for the F1 score and 86.8% for IoU. The other models exhibited detectability ratios between 80% and 91% for the F1 score and 71% and 84% for IoU, respectively. Regarding efflorescence, the F1 scores and IoUs were larger than those of cracks for all models, owing to the wide shapes of efflorescence. CGNet achieved the best F1 score of 0.71 and IoU of 0.59, which were greater than those of the other models. The ratios were 91.7% for the F1 score and 94.5% for the IoU, which indicated that the performance of CGNet was close to that of DeepLabV3+. Similar trends were observed for rebar exposure and spalling. CGNet achieved the F1 score of 0.65 (ratio = 87.7%) and IoU of 0.47 (ratio = 79.9%) for spalling, and the F1 score of 0.75 (ratio = 95.8%) and IoU of 0.60 (ratio = 94.4%) for spalling. Although DDRNet-23-Slim is a heavyweight model that was expected to have better detectability, the detectability ratio was determined to be lower than that of the other models. As a result, CGNet showed the best performance in detecting damage, except for cracks. The lower performance of CGNet in detecting cracks could be solved by implementing proper post-processing algorithms, because the CGNet detected cracks thicker than the ground truths. As shown in, CGNet exhibited a relatively high recall and low precision, which meant that the crack objects were predicted to be thicker. If the cracks were predicted to be thick, post-processing algorithms that use general CV techniques such as subtraction [57] could, subsequently, help to obtain accurate crack objects. Considering the possible implementation of post-processing algorithms for cracks, CGNet could be considered the best segmentation model to detect all types of damage with reasonable accuracy.