###### Abstract  
Regular crack inspection plays a significant role in the maintenance of concrete structures. However, most deep-learning-based methods suffer from the heavy workload of pixel-level labeling and the poor performance of crack segmentation with the presence of background interferences. To address these problems, the Deformable Oriented YOLOv4 (DO-YOLOv4) is first developed for crack detection based on the traditional YOLOv4, in which crack features can be effectively extracted by deformable convolutional layers, and the crack regions can be tightly enclosed by a series of oriented bounding boxes. Then, the proposed DO-YOLOv4 is further utilized in combination with the image processing techniques (IPTs), leading to a novel hybrid approach, termed DO-YOLOv4-IPTs, for crack segmentation. The experimental results show that, owing to the high precision of DO-YOLOv4 for crack detection under background noise, the present hybrid approach DO-YOLOv4-IPTs outperforms the widely used Convolutional Neural Network (CNN)-based crack segmentation methods with less labeling work and superior segmentation accuracy. computer vision; convolutional neural network; YOLOv4; image processing techniques; crack detection; crack segmentation +
Footnote †: journal: Medical  
time of box annotations is much less than that of pixel annotations [18]. Moreover, the IPTs-based segmentation method will, in general, exhibit higher computational speed and lower resource consumption compared with the CNN-based segmentation method. Therefore, the hybrid approach can merge the advantages of both CNN-based object detection and IPTs-based segmentation methods, leading to less labeling cost and higher efficiency for crack segmentation. However, the performance of the hybrid approach will significantly deteriorate with the presence of false or missed crack region detection. Therefore, object detection is expected to locate the crack regions precisely so as to improve the performance of the hybrid approach effectively. Actually, the existing object detection methods, e.g., Faster Region-CNN (Faster R-CNN) [19], Single Shot Multibox Detector (SSD) [20], and You Only Look Once (YOLO) [21; 22] are not that effective in detecting cracks. To tackle this problem, [PERSON] et al. [23] introduced a feature extraction network, namely Resnet, with up to 101 convolutional layers, to enhance the performance of the original Faster R-CNN for crack detection. Similarly, [PERSON] et al. [24] developed an improved Faster R-CNN by incorporating the Inception Resnet v2, which consists of more convolutional layers. In contrast to introducing complex feature extraction networks, [PERSON] et al. [25] utilized several lightweight Squeeze and Excitation (SE) blocks to achieve better performance in crack detection with Faster R-CNN. In addition, [PERSON] et al. [26] introduced receptive field blocks with a few convolutional layers to enhance the feature extraction capability of the network, leading to an improved SSD with higher accuracy for crack detection. [PERSON] et al. [27] enhanced the traditional YOLOv4 through the utilization of convolutional attention blocks, which can identify crack regions from multi-object images. However, the aforementioned studies involve complex networks for crack detection, owing to the utilization of deep feature extraction networks or the incorporation of feature extraction blocks. Moreover, the horizontal bounding boxes adopted in the above object detection methods fail to tightly locate cracks with various distribution directions, making network training challenging with the presence of background interferences. In this study, several deformable convolutional layers [28] are incorporated into the traditional YOLOv4 to enhance the ability of feature extraction for weakly patterned cracks while keeping the improved network lightweight without loss of accuracy.