# A Rapid Bridge Crack Detection Method Based on Deep Learning
## 1 Introduction
Based on the current research status, it is obvious that obtaining large amounts of high-quality data on cracks is still a time-consuming and costly task [19]. Additionally, it is worth discussing which form of neural network is most suitable for the rapid detection of bridge cracks. Therefore, the DCGAN is employed to generate a large number of artificial crack samples to expand the dataset. As a representative artificial neural network (ANN), DCGAN can mining the potential distribution of image data and achieve image data fitting so that the neural network can produce high-quality realistic images based on learned image features [20; 21]. In terms of application fields, generative adversarial networks, a popular means of dataset augmentation and generation, have been widely used in medical fields, safety inspection, agricultural production, and other fields [22]. An imbalanced fault diagnosis method based on the generative model of DCGAN was proposed by [PERSON] et al. [23] to solve the problems of limited datasets. In order to expand fake fingerprint data, [PERSON] et al. [24] proposed a method to investigate whether a fake fingerprint generated by DCGAN was similar to a fake fingerprint from the dataset. At present, image generation technology is still relatively rare in the field of roads, and the intelligent detection of road cracks is also in its exploratory stages. For example, the collected images of roads often have complex backgrounds and are heavily affected by light, and conditions such as cracks are often difficult to distinguish [25]. Therefore, it is of value to study bridge crack data augmentation methods based on DCGAN. YOLOv5, which is considered to be an efficient and stable target detection neural network, is used to achieve the rapid detection of bridge cracks [26; 27]. In recent years, the YOLOv5 object detection architecture has gained increasing attention in the field of computer vision due to its outstanding performance in detecting various objects in real-time scenarios. The utilization of YOLOv5 for bridge crack detection brings several advantages [28; 29; 30; 31]. Firstly, YOLOv5 offers a high accuracy in detecting and localizing cracks on bridge surfaces. With its advanced anchor-based and anchor-free mechanisms, YOLOv5 can effectively identify and delineate fine cracks, regardless of their length, width, or orientation. This enables engineers and researchers to obtain precise information regarding crack locations and sizes. Secondly, the real-time capabilities of YOLOv5 enable rapid crack detection, aiding in efficient inspections. Its speed-optimized architecture allows inspectors to quickly assess the conditions of bridges. This reduces the time and resources required for manual inspections, making crack detection more time-efficient and cost-effective. Moreover, YOLOv5 can adapt to varying crack features and textures, ensuring consistent and reliable crack detection across different bridge types. This flexibility eliminates the need for multiple detection techniques, simplifying the crack detection process. This study shows that the combination of DCGAN and YOLOv5 can carry out the detection of engineering defects rapidly and accurately. The sections of this study are structured as follows: Section 2 presents a detailed introduction of the establishment of the dataset and the structure and theory of DCGAN and YOLOv5. In Section 3, the training process, training environment, and predicted results are presented. Subsequently, Section 4 discusses the research motivation, limitations of this work, and future research directions. Finally, Section 5 provides the concluding remarks for this study.