# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 4 Results and Discussion
### Predicted Results Visualization  
For the qualitative analysis of the segmentation performance of all the models, some prediction results for the test dataset are illustrated in Figure 9. The results are presented for each model corresponding to each damage class, with the raw image and pixel label ground truth from top to bottom. From the visual inspection, all models successfully predicted damage; however, some false alarms or FPs were observed in some prediction images. In the crack predictions, the models predicted the cracks but with a different pattern, compared to the ground truth. In the ground truth, the crack was labeled as non-continuous with a fine texture; however, the predictions were continuous and coarser than the ground truth. In the efflorescence class, it is evident from Table 1 that all models achieved better accuracy and an IoU of more than 50%. However, in the prediction, models predicted efflorescence with the same pattern as the ground truth, but some labels were missing and predicted as background, instead of efflorescence. The third column in Figure 9 shows the prediction results for the spalling class. In the background of the spalling image, there are parts of the climber or creeper plant. As a result, of this noisy and complex background, some false predictions were observed in the results of ESNet and DDRNet-23-Slim; however, overall, each model achieved better accuracy in the spalling class. The rebars in the concrete structure were mainly exposed after spalling; hence, the fourth column in Figure 9 displays the rebar exposure surrounded by spalling. All the models predicted rebar exposure with a minor pattern difference when compared with the ground truth.  
Figure 9: _Cont._  
Figure 10 illustrates some of the prediction results for all models with multiple classes and background noise. Noise in the background, or varied lighting conditions, in an image can significantly impact the performance of a deep learning model. For this purpose, the performance of each model was analyzed and compared. The first example includes the prediction of crack and spalling classes in parallel. Only CGNet predicted both types of damage in the lightweight models; however, certain spalling pixels were not predicted. DDR-23-Slim and DeepLabv3+, meanwhile, predicted spalling in the same manner as CGNet. In the case of spalling and rebar exposure, all models, except CGNet and DeepLabV3+, failed to detect rebar exposure. In the last example, in Figure 10, the image contained only cracks, and in the background, some stones were visible at the right corner of the image. In the prediction of all models, except DeepLabV3+, there were false alarms and missing crack labels. ESNet and DDRNet-23-Slim failed to predict cracks. CGNet predicted cracks more accurately than ENet; however, stones in the background were predicted as spalling. As in all examples, the DeepLabV3+ model predicted damage effectively, irrespective of any background conditions. Comparing the performance of other models with DeepLabV3+ indicated that the CGNet model predictions in complex background conditions, or with multiple labels, were better than those of the other models.  
Figure 10: _Cont._  
Figure 9: Example of prediction results of all models with original images and ground truths.