# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 1 Introduction
To improve the efficiency of detecting and locating an object (such as a crack), more advanced object detection technology needs to be further explored. Region-based classification or object detection provides a state-of-the-art method for SHIM. This method creates a bounding box around the region of interest (ROI), such as cracks, spalls, components, etc. Common methods include two-stage and one-stage algorithms. Two-stage algorithms include region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN. The R-CNN has been applied to post-event building reconnaissance with an accuracy of nearly 60% [28], and the related research shows that its computation speed is low [29]. As an advanced version of the R-CNN, the Fast R-CNN is proposed to improve computational efficiency and accuracy, and it is used to detect different defects and locations of concrete structures [30]. Then a faster R-CNN (Faster R-CNN) is proposed by introducing a region proposal network (RPN) [29], and it is employed to automatically detect structural components of the RC bridge system [31] and cracks for asphalt pavements [32]. Although the above studies demonstrated the ideal accuracy of two-stage detectors, their detection is not very fast. High-speed detection is essential for the development of real-time automatic inspection systems, which seem to be the future trend of the industry [33]. Due to the above limitations, one-stage detectors have been proposed. The popular one-stage models include You Only Look Once (YOLO) and single-shot multi-box detector (SSD). These are faster than two-stage deep learning object detectors, such as region-based CNN (i.e., Faster R-CNN) [34]. Recent studies employed the YOLO network to detect multiple concrete bridge defects [6] and pavement cracks [35]; the SSD was also applied to detect road defects in real-time [25]. However, some researchers found that locating defects using one-stage detectors could compromise the accuracy of the detection [36]. The above research results show that the crack detection method based on an object detection algorithm has become a hot topic, and both two-stage and one-stage algorithms need a feature extractor (i.e., a CNN model), but the effect of different feature extractors on image feature extraction is not clear. With the continuous updating of neural networks, some well-known CNN models are widely used to complete different situations, which will provide more suitable feature extractors for defect detection. Therefore, the influence of different feature extractors on crack detection is a problem worthy of in-depth investigation. The application of transfer learning technology provides a state-of-the-art method for feature extraction. As the well-known CNN models have strong feature extraction ability, it will save a lot of time (no-training process) to use these CNN models as feature extractors. Therefore, this paper compares various CNN models (e.g., 'alexnet','resnet18', etc.) as the feature extractor of the YOLO_v2 to identify a model with high precision and fast computational speed. Meanwhile, parametric studies are implemented to confirm the effect of the relevant parameters on the detection results. Finally, the influence of different feature layers on the detection results is revealed by feature visualization.