# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 1 Introduction  
Many of the existing concrete structures built during the 1960-70s are rapidly nearing the end of their service life [1]. It is estimated that nearly 10% of bridges built during this time have been repaired in the United States [2]. In Korea, the number of buildings over 30 years old was evaluated as 3.8% in 2014, reaching 13.8% by 2024, and 33.7% by 2029 [3, 4]. Likewise, concrete structures are often exposed to aggressive environments, fatigue stresses, and cyclic loading that initiate cracks on the surfaces [5, 6]. The cracks in structures have a significant impact on durability and make it easy for external aggressive substances to reach the reinforcement bars and cause corrosion [7, 8]. In addition, cracks in the structures also reduce the local stiffness and cause material discontinuities [9, 10]. Therefore, cracks must be detected and repaired in time in order to maintain the reliability and performance of the structure. Generally, crack detections were performed by non-destructive and destructive tests [11]. Visual inspections combined with surveying equipment were manually performed to detect cracks in the structures [12]. A PZT-based electro-mechanical admittance method combined with FEM analysis was enacted to quantitatively identify the damage caused by concrete cracking and steel yielding of flexural beams subjected to monotonic and cyclic loading [13]. [PERSON] et al. [14] developed a wireless impedance/admittance monitoring system to identify the incipient damages caused by concrete cracking. In addition, non-destructive testing techniques such as infrared, thermal, ultrasonic, laser, and radiographic tests were also used to detect and analyze the crack development in concrete structures [15]. Although the above methods provide reliable crack detection results, they are difficult and time-consuming to perform because they require large instrumentation, and are expensive and labor intensive [16]. To overcome theshortcomings of the manual methods, several image processing methods were developed to provide automated crack detection and visualization in concrete structures. Most of the image processing methods used filtering, thresholding, and feature extraction techniques to identify and localize the cracks [17, 18, 19, 20, 21, 22]. Furthermore, the crack regions were separated by fuzzy transforms and segmentation algorithms [21]. Although image processing methods were effective in detecting cracks, the real-time applicability in structures was limited due to the variations in external environmental factors such as light, shadows, and rough surfaces. To improve the performance of image processing techniques, machine learning algorithms were developed through pattern recognition and extraction [23]. Machine learning algorithms such as support vector machine (SVM) and artificial neural network (ANN) have also been explored to detect cracks in the concrete structures [24, 25, 26, 27]. A local entropy-based thresholding algorithm was proposed that automatically detects spalled regions on the surface of the reinforced concrete columns [28]. In addition, the length and width of cracks were also measured using a local binary pattern (LBP) algorithm [29]. Machine learning algorithms consisting of feature extraction and classification were used to extract relevant crack features. The machine learning algorithm extracts only a few layers of features, and the algorithm might not provide accurate crack detection results if the extracted features do not reflect the cracks. Deep learning algorithms such as convolutional neural networks (CNNs) have been used in many studies for crack detection and classification to improve the feature extraction process. CNN models can extract relevant features from the input data through multilayer neural networks, which are more advantageous than the existing limitations of image processing and machine learning methods. CNN-based crack detection was performed for the safety diagnosis and localization of damages in concrete structures in [30].