## 1 Introduction  
Cracks are the most frequent defects in concrete structures, and their formation and propagation significantly deteriorate the safety and durability of infrastructures [1]. The accurate detection of the location and severity of cracks is crucial for the operational safety and long-term performance of structures, which depend on the detection and quantification of crack parameters, and machine vision-based crack detection technology has been widely applied due to its high efficiency, convenience, and non-contact advantages [2]. Traditional image processing methods (white-box methods) and artificial intelligence techniques (black-box methods) are replacing manual inspection, measurement, and analysis [3] and have drawn considerable attention from researchers. White-box methods have the advantages of a low computational cost, traceability, transparency, and interpretability; they do not require large volumes of crack image datasets, and they have been widely used in crack image detection for more than a decade. [PERSON] introduced an algorithm for a multi-angle, multi-structure element morphological filter based on morphological filter techniques, and the method could preserve details in images and enhance the effectiveness of crack identification and extraction [4]. [PERSON] et al. improveddetection accuracy using the OTSU method and by adaptively setting Canny threshold parameters [5]. [PERSON] et al. proposed a skeleton-marker method to remove binary noise and segment concrete cracks and achieved more reliable crack detection results than previous methods [6]. The acquisition of concrete crack images often takes place in complex environments, including illumination variations, stains, and oil residues on the cracks' surfaces and, sometimes, outer object occlusion. Unfavorable environmental factors may influence the value of the gray threshold, the area threshold, and the connectivity in image processing and pose threats or challenges in crack detection and parameter calculation, all of which limit the generalization capability of image segmentation [7]. In AI-based crack segmentation methods, the deep learning method demonstrates excellent performance and has become popular as the mainstream method due to its high accuracy, robustness, and strong generalization capabilities [8; 9; 10; 11; 12]. [PERSON] et al. first applied U-Net, a concrete crack segmentation method optimized with the Adam algorithm, and achieved more accurate crack image segmentation than prior to its integration, with effectiveness and robustness [8]. [PERSON] introduced a dual-coding network, DTFC-Net, and it outperformed other state-of-the-art segmentation networks and exhibited superior generalization in complex scenes [7]. [PERSON] et al. proposed the CBAM-Unet algorithm in bridge crack identification, which could effectively reduce detection costs and enhance efficiency [9]. [PERSON] et al. presented an improved deep fully convolutional neural network named CrackSegNet with a higher accuracy and generalization capability, and it made tunnel detection and monitoring efficient and cost-effective [10]. In another study, a combination of white-box methods and black-box methods yielded superior pixel-level segmentation results [11]. [PERSON] et al. integrated deep learning with a digital image processing method in crack recognition [12], and the model could automatically locate and extract cracks by means of a deep convolutional neural network combined with local threshold image segmentation. Also, this approach could precisely locate the maximum crack width's position and calculate its width. Deep learning-based concrete crack segmentation depends on deep convolutional neural network architecture, datasets, and evaluation metrics [13]. The dataset is the digital basis for the data-driven image segmentation method, also called the data-dependent image segmentation method, where dataset quality and quantity are equally important.