## 5 Concluding Remarks  
This paper makes an effort to address the challenge of limited datasets in the instance of segmentation models in the field of bridge crack detection. Considering the challenge and impracticality of building a large dataset composed entirely of multiple cracks, we propose a relative-breakpoint crack annotation method to support the lightweight crack detection model, which requires a limit on the training samples in order to improve the model's feasibility and generalization. Several highlights of the findings can be drawn as follows:  
* A lightweight bridge-crack identification solution is proposed for bridge routine inspection and maintenance, especially for bridges with poor accessibility. The proposed method mainly contains a crack annotation and crack detecting model training procedure.
* In-depth analysis has been conducted on how the crack annotation form effects the pre-trained model behavior. The proposed relative-breakpoint-based annotation strategy can improve the performance of an instance segmentation model, especially under the complex interlaced-crack scenes; it can effectively distinguish such crisscrossed cracks.  
Figure 8: Crack detection results.  
Hence, this method can enable various instance-segmentation models to effectively distinguish multiple cracks at intersections, as well as improve the efficiency and accuracy of subsequent analysis.
* Classic Mask RCNN and Yolact were both employed to establish crack instance segmentation models to carry out the crack detection, localization and segmentation. With the complex validation set, the model localization accuracy and recall rates can reach 88.4% and 89.2%, respectively. The results demonstrate the effectiveness of the proposed breakpoint-based annotation in distinguishing complex interlaced cracks for both crack instance segmentation models.  
In summary, the proposed relative-breakpoint-based annotation has the advantages of an ability to distinguish between cracks of different scales and a friendliness for model convergence. In general, our method normalizes the format of the crack instance segmentation dataset and strengthens the accuracy and generalizability of the instance segmentation model from the data level. With the standardized and improved labelling rules, the crack identification model can be trained to its intended ability with fewer samples.  
To further verify the robustness and versatility of our method in detecting cracks in concrete structures, validation of the method is planned, specifically, by applying it to specific concrete bridges, evaluating its generalization ability through testing on various types of crack conditions and exploring ways to enhance its accuracy.  
Conceptualization, W.X. ([PERSON]); Funding acquisition, W.X. ([PERSON]); Investigation, W.X. ([PERSON]); Methodology, Y.Z. and W.X. ([PERSON]); Project administration, Y.Z. and W.X. ([PERSON]); Software, Y.Z. and W.X. ([PERSON]); Supervision, Y.Z. and C.S.C.; Validation, Y.Z.; Writing--original draft, W.X. ([PERSON]); Writing--review and editing, Y.Z. and C.S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Projects 52022021, 51978160, 52108118), the Key Laboratory of Roads and Railway Engineering Safety Control (Shijiazhuang Tiedao University), the Ministry of Education (STDTKF202101), and the Key Research and Development Program of Jiangsu Province of China (Project BE2021089).  
Not applicable.  
Data availability Statement: Data will be made available on request.  
The authors declare no conflict of interest.