## 5 Conclusions  
To avoid the time-consuming process of pixel-level labeling brought by CNN-based crack segmentation methods and to improve the performance of crack segmentation of concrete structures with the presence of background interferences, a novel hybrid approach is presented in this study by combining DO-YOLOv4 for crack detection and IPTs for crack segmentation. Owing to the use of deformable convolutional layers and multiple oriented bounding boxes, the proposed DO-YOLOv4 has a strong learning ability for crack characteristics with its lightweight network and is capable of locating a wide variety of cracks at high accuracy, even with background noise. Following this, the present hybrid approach DO-YOLOv4-IPTs can effectively reduce the labeling cost by means of box annotations and meanwhile improve the performance of crack segmentation, in particular for the images with very tiny cracks and/or crack-like interferences.  
For the experiments conducted in the present study, the AP metric of DO-YOLOv4 goes as high as 80.43% and is 21.54%, 39.39%, 35.27% and 27.33% higher than those of Faster R-CNN, SSD, YOLOv3 and YOLOv4, respectively, indicating the high accuracy of DO-YOLOv4 for crack detection, and with that, the hybrid approach DO-YOLOv4-IPTs turns out to have the highest mean PIoU metric of 71.64% with only 1/10 of the mean time cost of labeling for the traditional methods, showing the promising performance of crack segmentation by DO-YOLOv4-IPTs.  
Despite the success achieved above, it has been observed that the parameters adopted in IPTs, namely the mean pixel intensity \(I_{\text{m}}\), the constant \(C\), and the area threshold \(AR_{\text{thr}}\), need to be determined by manual intervention with prior knowledge. The values of such parameters will have a certain influence on the crack segmentation results by IPTs within the bounding boxes. The adaptive optimization of such parameters is crucial to the automatic crack segmentation under different circumstances, which deserves further research for the present approach.  
Furthermore, the robust learning ability facilitated by the deformable convolution and the oriented bounding boxes can be further utilized to address object detection challenges in other fields, such as retinal vessel detection, multi-class concrete defect identification, etc.  
Z.H.: Methodology (lead), Formal analysis, Investigation, Writing--original draft. C.S.: Resources, Methodology, Validation, Writing--review and revising, Funding acquisition. Y.D.: Conceptualization, Methodology, Writing--review and editing, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
The research is funded by the National Natural Science Foundation of China (52308314), Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology (2021B1212040003), Guangdong Basic and Applied Basic Research Foundation (2022A1515010174, 2023A1515030169), Guangzhou Science and Technology Program (202201010338), and Guangzhou Science and Technology Program (201804020069).  
The code of DO-YOLOv4-IPTs has been uploaded to the website [[https://github.com/DO-YOLOv4-IPTs/main](https://github.com/DO-YOLOv4-IPTs/main)]([https://github.com/DO-YOLOv4-IPTs/main](https://github.com/DO-YOLOv4-IPTs/main)) (accessed on 8 February 2024), allowing interested readers to apply it in their respective research fields.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.