###### Abstract
Therefore, many scholars have used the above-mentioned deep learning techniques to achieve automated detection of bridge defects. [PERSON] et al. [15] constructed an end-to-end and embedded ASPP structure convolutional neural network to achieve object detection of bridge cracks, and the F1 score of the model reached 87.71%. [PERSON] et al. [16] generated multiscale feature maps in the encoder of a traditional semantic segmentation network and used adversarial learning to complete the semantic segmentation of bridge cracks, achieving an f1 score of 88.936%. [PERSON] et al. [17] proposed APLCNet, based on Mask RCNN, for sidewalk crack detection, with an F1 score of 93.53% on a CFD dataset [18]. [PERSON] et al. [19] proposed CrackSeg for segmentation of sidewalk cracks. In the same year, [PERSON] et al. [20] improved CrackSeg and applied it to the detection of bridge pier cracks; its accuracy for crack segmentation reached 90.92% and its recall rate reached 97.47%. [PERSON] et al. also improved CrackSeg, using the ResNeXt [21] backbone network, and proposed PCSN for bridge crack segmentation. The accuracy reached 83%. In addition, research on the interpretability of deep learning in bridge defect detection has also made new progress. [PERSON] et al. [22] proposed DL-based methods for identifying defects in reinforced concrete bridges, offering humanly explainable interpretations using XAI techniques such as CAMs. In terms of datasets, [PERSON] et al. [23] published a high-quality crack dataset called DeepCrack Datasets, and proposed DeepCrack, an end-to-end deep convolutional neural network for automatic crack detection. DeepCrack outperforms state-of-the-art methods, achieving an average F-measure of over 0.87 on three challenging datasets. [PERSON] et al. [24] made a pavement crack dataset, named CRACK500, with 500 images. And [PERSON] et al. proposed FPHBN, a novel network architecture for automatic pavement crack detection. In terms of crack geometric parameter extraction, [PERSON] et al. [25] developed a novel method to detect and quantify cracks with high accuracy and efficiency. Their method uses a U-net network and a thinning algorithm to extract the geometric parameters of cracks, which contributes to crack detection and geometric parameter extraction. [PERSON] et al. [26] first used YOLO to locate the bridge cracks, and then used a digital image method for edge detection in the locating area to achieve the measurement of crack dimension. [PERSON] et al. [27] used DeepLab to perform sematic segmentation on crack images, which can better describe the key dimension information, such as the length and width of the cracks. Although the algorithms and datasets for crack segmentation have been initially effective in bridge crack detection, they are mainly limited in the semantic segmentation of cracks, which cannot satisfy the need to distinguish different cracks. As mentioned above, although the improvement of CrackSeg substantially improves the segmentation accuracy of bridge cracks, its semantic segmentation characteristics cannot satisfy the localization of the intersecting longitudinal cracks which mainly affect bridge safety. [PERSON] et al. proposed APLCNet [17] on the basis of Mask RCNN; although it completes the segmentation and localization of cracks, its dataset rarely involves complex intersecting cracks. Therefore, further research is urgently needed for instance segmentation of complex cracks with interlaced vertical and horizontal dimensions, in terms of both data and algorithms. The CRACK500 [24] and DeepCrack [23] datasets are both datasets used for semantic segmentation. In contrast, there is a relative lack of research on crack instance segmentation, especially regarding datasets. Though [PERSON] et al. [28] annotated 500 images from other datasets to create a dataset for crack instance segmentation, it did not mention the criteria used to distinguish different cracks at the junction intersection.