# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
## 3 Review of DL and ML Safety Applications  
### Shm  
#### 3.1.1 Vision-Based Damage Detection  
In recent years, researchers have used computer vision-based methods to conduct the visual inspection of surface defects and have attested considerable merits [33; 34; 35]. These methods are primarily based on image processing techniques (IPTs), such as histogram transformation, texture recognition and edge detection [1; 36]. However, these methods are vulnerable to lighting condition changes and image distortion issues. To enhance the performance of IPT-based approaches for defect detection, researchers have integrated ML algorithms [37; 38; 39]. Technically, ML algorithms can efficiently classify different damage features extracted from IPTs. ML-based methods mostly focus on identifying typical structural defects such as cracks [40; 41; 42; 43; 44; 45; 46; 47], rusting [48; 49], spalling [50; 51] and loose bolts [52]. Nevertheless, these methods require defect features to be clearly defined and extracted using proper classifiers. Overall, these methods lack efficiency, feasibility and accuracy. Rapidly developing DL techniques are expected to solve the problems mentioned above. The CNN, as an end-to-end model, can improve the efficiency of defect detection and localization significantly because it can learn the defect features automatically from the labeled defects in the training samples. Normally, the process of using a CNN to determine defects in images is as follows: a fixed-size sliding window is used to scan and separate the image into small patches, and then a well-trained CNN is used to detect the defects on each small patch separately. Because the scales and shapes of defects may vary, it is difficult to find an appropriate window size to fit all kinds of them in practice. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size** & **Silhouette** & **LSI** & **LLR** & **MI** \\ \hline
82 & 0.844 & fuzzy inference & body posture & jobsite video stream \\
66 & 0.977 & crack detection & deep learning-based crack & rapid damage assessment \\
54 & 0.895 & computer vision & classification & tunnel construction project \\
39 & 0.831 & deep learning & deep learning-based crack & field surveillance video \\
38 & 0.911 & construction workers & construction worker & biomechanical analysis \\
38 & 0.896 & automated 2d detection & support vector machine & automated 2D detection \\
37 & 0.979 & parameter optimization & automated concrete detection & deep learning \\
13 & 0.993 & improved deep learning approach & construction accident report & deep learning \\
6 & 0.971 & concrete crack detection & pixel-level bridge & deep learning \\
4 & 0.996 & multiple vehicles & multiple vehicles & personal protective equipment \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top-ranked clusters derived from three algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Co-Citation Count** & **First Cited Year** & **Cited Journals** & **Impact Factors (SCI)** \\ \hline
271 & 2010 & AUTOMAT CONSTR & 3.13 \\
229 & 2010 & J COMPUT CIVIL ENG & 1.42 \\
187 & 2011 & LECT NOTES COMPUT SC & 1.17 \\
184 & 2010 & ADV ENG INFORM & 3.10 \\
161 & 2010 & COMPUT-AIDED CIV INF & 5.29 \\
142 & 2012 & J CONSTR ENG M & 2.00 \\
107 & 2016 & SENSORS-BASEL & 3.32 \\
104 & 2011 & INT J COMPUT VISION & 6.56 \\
97 & 2014 & EXPERT SYST APPL & 5.23 \\
81 & 2018 & IEEE ACCESS & 4.64 \\
77 & 2016 & NATURE & 40.13 \\
76 & 2015 & J MACH LEARN RES & 3.13 \\
74 & 2018 & NEUROCOMPUTING & 5.19 \\
72 & 2015 & COMMUN ACM & 4.46 \\
69 & 2016 & ENG STRUCT & 2.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cited journal frequencies (Impact Factors (IFs) are based on the first cited year). To overcome the drawback above, a region-based CNN (R-CNN) [53] was proposed to replace the sliding windows method. The R-CNN is a two-stage detector. First, it employs a selective search approach [54; 55] to generate region proposals. Then, the defect features can be extracted from the regions for classification and be highlighted by bounding boxes.