# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 5 Conclusions  
This study mainly focused on the relationship between ECDs and deep learning models. For this purpose, the performances of various lightweight (ENet, CGNet, and ESNet) and heavyweight (DDRNet-23-Slim) semantic segmentation models were compared by benchmarking the DeepLabV3+ (ResNet-50) model. All models were trained without using pre-trained weights, except for DeepLabV3+, on a real concrete dataset with four damage classes: cracks, efflorescence, rebar exposure, and spalling. The performance of each trained model was validated using a 400-image testing dataset that included all the four damage classes. Following are the conclusions based on the study:  
* By benchmarking the IoU and F1 score of DeepLabV3+, CGNet achieved a higher detectability ratio regarding both the F1 score and mIoU metrics (varying from 77% to 95%) compared with the other models, except for crack segmentation. It was evi  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sr. No**} & \multirow{2}{*}{**Models**} & **Parameters** & \multirow{2}{*}{**FLOPs(G)**} & **Inference Speed** \\  & & **Millions (m)** & & **Milliseconds (ms)** \\ \hline
1 & ENet & 0.4 & 6.98 & 140 \\
2 & CGNet & 0.5 & 9.8 & 60 \\
3 & ESNet & 1.6 & 41.42 & 96 \\
4 & DDRNet-23-Slim & 5.7 & 16.27 & 85 \\
5 & DeepLabV3+ & 17 & 156.67 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational cost comparison of all trained models.  
dent that the decoder module of CGNet did not have any trainable layers; thus, the prediction of small objects, such as cracks, omitted some essential information.
* Among all the models, CGNet exhibited a better inference speed of approximately 60 ms per image of 680 px \(\times\) 680 px, which required only 9.8G FLOPs.
* The tradeoff between model parameters, inference time, and accuracy indicated that CGNet was the best among the four models and could be embedded in ECDs for on-site damage detection.  
This study demonstrated the potential of lightweight deep learning applications for automated structural inspections. Considering the ongoing evolution and improvement of deep learning methods and algorithms, more ideal algorithms could be embedded in ECDs to obtain more advanced detection outcomes in the future. Furthermore, the damage in the dataset could be extended by adding segregation to the concrete and corrosion to the rebar.  
Conceptualization, M.T. and S.C.; methodology, M.T. and B.K.; software, M.T. and B.K.; validation, M.T., B.K., J.H., S.-H.S. and S.C.; formal analysis, M.T.; data curation, J.H., and B.K.; writing\(-\)original draft preparation, M.T.; writing\(-\)review and editing, S.-H.S. and S.C.; visualization, M.T.; supervision, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Agency for Infrastructure Technology Advancement (KAIA) Grant (21 CTAP-C163726-01).  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from inspection reports of Seoul Facilities Corporation and are available with the permission of Seoul Facilities Corporation.  
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA), grant funded by the Ministry of Land, Infrastructure, and Transport (Grant 21 CTAP-C163726-01).  
The authors declare no conflict of interest.