## 2 Related Work  
### CNN-Based Object Detection  
Typical CNN-based object detection methods are widely used for recognizing and locating objects with bounding boxes, which can be classified into two main categories: the two-stage approach and the one-stage approach. For the two-stage object detection approach, potential regions that may contain objects are first proposed, and then the features in these regions will be used for box classification and regression. The R-CNN [29] is the early version of the two-stage approach, which needs to repeatedly extract features for different regions, resulting in a high computational cost. To improve the detection efficiency, the Fast R-CNN [30] and Faster R-CNN were proposed successively. In particular, the Faster R-CNN is a representative two-stage method for its high detection accuracy. [PERSON] et al. [31] used Faster R-CNN to detect five types of bridge defects, including concrete cracks, steel corroisons, delamination, etc., and the experimental results showed the high accuracy of Faster R-CNN in defect detection. [PERSON] et al. [32] showed that Faster R-CNN could effectively locate cracks under complex backgrounds, even with the presence of handwriting scripts. [PERSON] et al. [33] enhanced Faster R-CNN through the feature fusion technique, which was capable of detecting cracks on the tunnel surface with high precision. Despite the ideal accuracy of Faster R-CNN, it still has the problem of low detection speed due to the region proposal step.  
As high-speed detection is essential for the development of real-time automatic inspection systems, the one-stage approach was proposed by removing the time-consuming region proposal step. The SSD and YOLO are two well-known one-stage methods that have been applied to crack detection in bridges [34] and pavements [26; 35; 36] in recent years. Generally, the one-stage approach outperforms the two-stage approach in terms of detection speed but compromises in detection accuracy. To tackle this problem, the YOLOv3 [37] was proposed by using several effective techniques, such as the short-cut connection [38], feature pyramid [39], and multi-scale detection [20]. [PERSON] et al. [40] showed that YOLOv3 achieved comparable accuracy to Faster R-CNN without sacrificing computational efficiency. To further enhance both detection accuracy and speed, the YOLOv4 was proposed with the advanced network architecture consisting of Cross Stage Partial Darknet53 (CSPDarknet53) for feature extraction, Spatial Pyramid Pooling (SPP) and Path Aggregation Network (PAN) for feature enhancement and YOLO head for bounding box output [22]. On the basis of YOLOv4, [PERSON] et al. [41] developed a novel YOLOv4-FPM for real-time and accurate detection of bridge cracks, which combines the focal loss, pruning algorithm, and multi-scale dataset. Additionally, [PERSON] et al. [42] enhanced the traditional YOLOv4 by incorporating EfficientNet and depthwise separable convolution, leading to YOLOv4-ED with superior performance in terms of both detection accuracy and efficiency.  
Although the CNN-based methods have achieved great success in the area of object detection, there are still two critical issues that need to be addressed when applied to crack detection, i.e., how to establish a lightweight object detection model and how to reduce the influence of background interferences on network training for detecting line-shaped cracks with various propagation directions. These two problems will be solved by introducing deformable convolutional layers and multiple oriented bounding boxes, which will be elaborated in Section 3.