# 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
However, the proposed region may be overlapped and, therefore, it may increase the computational burden [56]. To increase the efficiency of object detection, a Fast R-CNN [57] and Faster R-CNN [58] were proposed successively. Researchers have currently employed these two-stage detectors to detect the surface damages of structures. For instance, [PERSON] et al. [56] used a Faster R-CNN to identify five classes of structural defects in both steel and concrete structures. A Faster R-CNN architecture modified by [PERSON] et al. [59] could determine the small concrete defects even in a complex image background. Although two-stage detectors can provide high accuracy, the generation of a region proposal hinders the detection speed. Therefore, two-stage detectors are hard to use for achieving real-time detection. Due to the above limitations, single-stage detectors (e.g., the single shot Multibox detector (SSD) [60] and you only look once (YOLO) [61]) were proposed by combining object classification and localization into one single convolution network. The removal of the proposal generating step is the main feature of single-stage detectors. Besides that, they can predict multiple bounding boxes simultaneously. Hence, the detection speed of single-stage detectors can be improved significantly. Similarly, YOLOv3 was used by [PERSON] et al. [5] to detect the cracks, pop-out, delamination and exposed rebar of the bridge with relatively high accuracy. However, using bounding boxes to identify the damage may not be suitable, as such rectangular boxes cannot determine the boundaries of the crack textures accurately. In this case, pixel-level semantic segmentation, which assigns classification labels to each image pixel instead of generating bounding boxes, allows for better defect localization and analysis [2]. A fully convolutional network (FCN) [19] is a pixel-by-pixel network for semantic segmentation. It predicts the class (crack or non-crack) of each pixel by using a deconvolution layer to upsample the last convolutional layer. Compared with the methods above, the pixel-level method can completely separate the damage feature from the background by highlighting each pixel of the defect. At present, the FCN method has already been exploited to identify concrete cracks [55; 62] and other structure defects [63; 64; 65]. For example, [PERSON] et al. [66] proposed an FCN-based, pixel-level asphalt pavement crack detector. Similarly, [PERSON] et al. [67] employed an FCN as a part of their developed network for road crack detection based on digital images produced by black box cameras. The results proved that an FCN is optimal for defect information segmentations. Except for segmenting the crack, it can also provide valuable damage information, such as crack widths for damage assessment. When annotating defects in an image sample, a small portion of non-cracked surfaces is likely to be labeled as defects. Such random errors are inevitable and not easy to measure. To reduce the impact of such uncertainty, [PERSON] et al. [68] combined an FCN and a Gaussian-conditional random field for pavement defect detection. The developed framework can address the uncertainty of defect labeling.