###### Abstract  
After years of service, bridges could lose their expected functions. Considering the significant number of bridges and the adverse inspecting environment, the urgent requirement for timely and efficient inspection solutions, such as computer vision techniques, have been attractive in recent years, especially for those bridge components with poor accessibility. In this paper, a lightweight procedure for bridge apparent-defect detection is proposed, including a crack annotation method and crack detection. First of all, in order to save computational costs and improve generalization performance, we propose herein a relative-breakpoint annotation method to build a crack instance segmentation dataset, a critical process for a supervised vision-based crack detection method. Then, the trained models based on classic Mask RCNN and Yolac are transferred to evaluate the effectiveness of the proposed method. To verify the correctness, universality and generality of the proposed crack-detection framework, approximately 800 images are used for model training, while nearly 100 images are saved for validation. Results show that the crack instance segmentation model can achieve a level of 90% for both accuracy and recall values, with a limited dataset. b +
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fields of computer vision, and its biggest advantage over traditional digital image methods is its ability to extract deep features of images. Consequently, deep learning can resist the noise of various complex detection environments and significantly improve detection accuracy after effective training. [PERSON] et al. [4] integrated SfM technique and a pre-trained CNN model to localize and classify the ROI of the same full-size road sign structure, which showed better performance than previous methods. [PERSON] et al. [5] used FCN and recursive structure (long- and short-term memory, LSTM) to automatically segment and label the different structural components of a bridge structure and work with synthetic video datasets. The above study demonstrates that the application of deep-learning-based computer vision techniques for bridge crack detection can effectively address the shortcomings of traditional digital image methods and computer vision techniques. Combined with the use of UAV technology, deep learning can achieve nondestructive data collection of the bridge components' defects. Processing the data collected by the UAV using deep learning can achieve automated crack identification. More importantly, deep learning can be combined with the GPS positioning module and the sensor information of the UAV to achieve accurate localization and dimension assessment of the bridge cracks' defects. The application of deep learning techniques in the field of crack detection is divided into object detection and segmentation. Object detection of cracks is performed in the form of rectangular boxes (bbox) used to locate cracks in a given image. The semantic segmentation indicates the category to which each pixel of the original image belongs. The output is a mask that reveals the category to which each pixel belongs. The instance segmentation distinguishes and counts the detected objects based on the semantic segmentation, and outputs a rectangular box (bbox) with the same meaning as the object detection, in addition to the mask. The core idea of the above tasks is to continuously extract the feature information of a given image through various network structures, which is, essentially, to perform matrix operations at multiple levels and finally obtain the coordinate data of bbox or mask data. The widely used algorithmic architectures for object detection include YOLO [6] series, Faster RCNN [7] and DETR [8]; semantic segmentation algorithmic architectures include Unet [9], DeepLab [10], etc. ; instance segmentation algorithmic architectures include Mask RCNN [11], Yolact [12], etc. The network models used by different algorithms for extracting features are Convolutional Neural Networks [13] (CNN) and Transformer [14].