# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 1 Introduction  
Timely detection of cracks in concrete structures is an important step of structural health monitoring (SHM) [1]. In recent years, due to aging and environmental impacts, surface cracks of infrastructures, especially concrete structures, are a hidden danger that needs to be focused on [2]. Therefore, it is necessary to detect the cracks of concrete structures to prevent any further losses in their durability. In general, inspectors collect images or videos through on-site optical instruments, process the collected data, and finally draw the inspection conclusions; this is an effective method for simple tasks, but it is unsuitable for large-scale inspection due to its low efficiency and high cost [3]. The detection of bridge surface defects based on images is highly repetitive work [4]. In the case of a large amount of data, manual detection is tedious, inefficient, and expensive [5]. Therefore, it is essential to investigate some automated methods, e.g., artificial intelligence (AI) technology, to undertake this labor-intensive work. AI technology provides a more advanced method for SHM, which has the ability to perform various tasks (such as classification or regression) with outstanding performance. A number of AI-based image processing methods can be used for bridge defectdetection [5; 6]. [PERSON] et al. detected cracks in an asphalt pavement surface using a support vector machine (SVM); [PERSON] et al. detected road cracks using random structured forests (RSF) [7]. Furthermore, as an emerging AI algorithm, the artificial neural network (ANN) [8] has been used to classify rail surface cracks [9], and detect potholes on an asphalt pavement surface [10], which has become a popular technique in the SHIM field. However, the practical application of this method is limited due to its slow convergence, over-fitting, and high computational cost, etc. [11]. Therefore, a fast and automatic feature extraction algorithm [12; 13] is needed to process the huge monitoring data [12]. The application of deep convolutional neural network (DCNN) algorithms further improve the detection method, and can automatically extract features from the raw data and gradually obtain advanced features through multiple processing layers [14]. In the field of SHIM, the CNN can extract the signal distribution features from the images of fused time and frequency domain information [15], extract the frequency features from the acceleration signals [12], and extract the structural damage features from the modal shapes [13]. Meanwhile, a DCNN uses partial connections and pooling of neurons, thus, requires less computation, has better robustness, which makes the DCNN an effective and fast SHIM method. A lot of research has been conducted for SHIM based on the DCNN [16; 17] by using vibration signals [12; 18; 19] and defect images [16; 20; 21]. In the field of defect images-based SHM [22], image classification is a popular method for automatic defect detection. The DCNN has been used in image classification for pavement cracks [23; 24], sewer defects [21], and road damages [25]. In further research to determine the location of defects, a basic method was proposed to scan the image with a fixed size sliding window and then apply the trained DCNN to each small window. With this method, [PERSON] et al. proposed a DCNN model to classify whether there are concrete cracks in each image block [16]. [PERSON] et al. employed a transfer learning model ('vgg-16') [26] to classify two types of defects: cracking and spalling [27]. These studies confirmed that the location of cracks in an image can be obtained by using a sliding window. However, the challenge of this sliding window method is to find the appropriate window size when dealing with defects of different scales. Moreover, the computational cost of this method is very high, because the DCNN classifier must be applied to every window in every image many times.