# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 4 Crack Detection Test  
In order to test whether the crack images obtained from the crowd sensing system can achieve satisfactory recognition results with deep learning models, we conducted recognition experiments using crack images taken by users in different scenes and with different shooting devices. As for the position accuracy in this study, it was estimated to be around 5-10 m. Previous studies have conducted researches on the accuracy of smartphone- and GPS-based location method. In terms of location accuracy by smartphone without post-processing, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) obtained an accuracy range of 7-13 m, [PERSON] (2018) reported an accuracy of around 10 m, [PERSON] et al. [PERSON] et al. (2018) found an accuracy range of 5-10 m, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) pointed out an accuracy of around 6.5 m. Furthermore, a meter-level accuracy could be achieved by the establishment of a differential correction method ([PERSON], 2018), an improved Hatch filtering method ([PERSON], 2018), and the RTK-based methods ([PERSON], 2018; [PERSON], 2018). Figure 12: Crack image acquisition. (**a**) Real-time picturing. (**b**) Picturing result. Given this challenge in crack location, part of the reason for the crack description function shown in Figure 13 was to provide assistance for crack location description. The uploaded user images have varying pixel sizes ranging from 4000 \(\times\) 3000 to 2000 \(\times\) 1000, and we randomly selected a few crack images for recognition. A U-Net-based model was trained to deal with the crack detection task for evaluation of the quality of the crack images captured in this way. The U-Net was proposed by [PERSON] et al. [48] for semantic segmentation. It has a U-shaped encode-decode architecture that can output the detection result in the same size with the input image. Thus, it is suitable to be trained for crack detection in a pixel-wise manner. The training process lasted for 200 epochs for the optimization of parameters in the network, and it was based on a publicly available dataset (published on 8 February 2021) at: [[https://doi.org/10.7910/DVN/RURXSH](https://doi.org/10.7910/DVN/RURXSH)]([https://doi.org/10.7910/DVN/RURXSH](https://doi.org/10.7910/DVN/RURXSH)). The code and the trained model were uploaded to Harvard Dataverse on 31 May 2023, and it is publicly available at: [[https://doi.org/10.7910/DVN/IFMJLE](https://doi.org/10.7910/DVN/IFMJLE)]([https://doi.org/10.7910/DVN/IFMJLE](https://doi.org/10.7910/DVN/IFMJLE)). Crack detection results of eight images with different crack geometries and backgrounds were adopted for illustration. The results of the actual image recognition using the deep learning model are shown in Figure 14. From the original images, it can be seen that the lighting conditions of mobile-phone-captured images are uneven, and different from the images used in the training process. Moreover, the large size of the images leads to the uneven distribution of pixels on the entire image, which is an important factor affecting the accuracy of recognition. Due to the large contrast between the crack and background noise, crack images can be recognized better in bright background images, such as the recognition results of Figure 14a-c in the figure below. The boundary between the background and crack is clear and the contrast is high, which enables the deep neural network model to achieve good recognition results. When the contrast between the background and crack is low, the model recognition is not accurate, error detection (marked with red boxes) happened, as shown in Figure 14d,e. In image e, part of the crack appears white due to the structural material, which causes the neural network to have obvious disconnection recognition result (marked with red boxes). Image d also has structural joint seams, which is a noise motif that is easily recognized as a crack in the network recognition.