# Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
## 1 Introduction
### State of the Art  
Several researchers have evaluated the use of artificial intelligence in the construction industry. Refs. [4, 5] evaluated the YOLOv4 object identification and classification algorithm to identify objects of interest in construction sites based on images from drones and static cameras. Eight classes of objects were identified in 1000 drone images and 1046 static camera images of a construction site, with an accuracy ranging from 78.8% to 82.8% and 73.56% to 93.76%, respectively. An enhanced convolutional neural network can detect objects in real-time. The database used by [6] consists of 10,000 images classified into \"worker\" and \"excavator.\" The results were measured by mean average precision (mAP); a 91% precision was obtained for the class \"worker\" and 95% for \"excavator\". A detection algorithm proposal was developed by [7]: Contrastive Res-YOLOv5 Network, or CORY-Net. This algorithm was compared to YOLOv3, Faster R-CNN, and Improved-YOLOv5 to verify the application's detection and classification accuracy. The generated database contains 22 classes and 12,000 images obtained from a construction site from the \"Safety-Helmet-Wearing-Dataset\" database, of which 7146 were manually classified as part of the training and testing process. Mean average precision (mAP) values of 63.7%, 69.7%, 70.2%, and 74.2% were obtained in the YOLOv3, Faster R-CNN, Improved YOLOv5, and the proposed CORY-Net algorithms, respectively. On the other hand, ref. [8] proposed a training process executed on a neural network to ensure an adequate social distance policy in a construction environment to identify people inside construction sites. They identified people performing activities in an upright and a crouched position using the YOLOv4 algorithm with an accuracy of 77.98%. More focused on controlling structural failures, ref. [9] proposed using convolutional neural networks to detect and classify cracks in concrete from images. The database consists of 40,000 images with a 256 \(\times\) 256 pixels resolution. This number of images started from 277 for the training and validation processes out of 332 raw images, and they were taken between 1.0 and 1.5 m from the objects of interest and with different lighting variations. The result reached 98% accuracy for detecting and classifying cracks. Also, ref. [10] proposed a framework based on the fusion of convolutional neural networks and the Naive Bayes algorithm, called NB-CNN, to detect cracks in underwater metallic surfaces in multiple video frames. The final database consists of 147,344 images with cracks and 149,460 without cracks. The proposed merger achieved a TPR (number of true positives among total positives) of 99.9% and a hit rate of 98.3%. This framework can detect small cracks with low contrast and a considerable variation in brightness; however, the neural network requires a large amount of data for training, at least 100,000, to avoid overfitting. Another research by [11] presented a model based on the Fast-RCNN architecture, Crack Deep Network (CrackDN), to detect sealed and unsealed cracks in complex road bottoms and compare it with the performance of the Faster-RCNN and SSD300. Images for the database were collected from different devices, smartphones, and mounted cameras to obtain a final amount of 12,000 images with a resolution of 500 \(\times\) 356 pixels. As a result, CrackDN had a higher mAP in detecting sealed and unsealed cracks of 97.48% and 97.56%, while Faster-RCNN obtained 96.41% and 95.46%, and SSD300 obtained 89.97% and 89.47%, respectively. A method based on a fully convolutional network (FCN) to detect image cracks is shown in [12]. The neural network was trained with a database of 500 manually classified images of 227 \(\times\) 227 pixels, obtaining an average accuracy of 90%. A convolution neural network to recognize cracks in asphalt surfaces is proposed by [13]. The database consists of 5000 manually classified 3D images of different pavement sections.