## 1 Introduction
[20] proposed the Crack Central Point Method (CCPM), combining UAVs and digital image processing for robust bridge crack detection with limited data. [PERSON] et al. [21] employed UAVs for high-rise-building crack detection, addressing motion blur with a GAN-based model. Although there have been significant advancements, the task of detecting pavement cracks remains challenging. Variations in image scale and complex background conditions contribute to the occurrence of leakage and misdetection. Quantifying pavement crack sizes allows for an accurate assessment of a road's current condition, which facilitates the implementation of preventive maintenance measures. Such measures not only prolong the road's service life but also diminish long-term maintenance expenses. Regarding crack width measurement, [PERSON] et al. [22] employed a hybrid image processing technique alongside a UAV equipped with ultrasonic displacement sensors, enabling working distance calculation and crack width estimation. [PERSON] et al. [23] employed a deep learning approach to analyze signals from distributed fiber optic sensors, enhancing the efficiency of detecting spatially distributed cracks. However, the application of fiber optic sensors is not feasible for large-scale pavement crack detection. [PERSON] et al. [24] utilized deep learning and laser sensors in a structured light application for detecting and quantifying surface cracks in concrete structures based on the laser beam's projection position on the surface. [PERSON] et al. [25] achieved bridge crack identification, segmentation, and width calculation using a UAV with a monocular camera aided by a Mask R-CNN. [PERSON] et al. [26] introduced a two-stage crack identification method combining a UAV with a laser range finder and image threshold segmentation for crack width determination. [PERSON] et al. [27] employed Faster R-CNN for crack region detection and used maximum entropy threshold segmentation and Canny edge detection to determine crack dimensions. [PERSON] et al. [28] developed a method to quantify cracks in various measurement poses using a full-field-scale UAV gimbal camera, addressing the challenge of image measurement relative to markers. However, current crack quantization methods face issues of either insufficient accuracy or high equipment costs. The existing research on pavement crack detection has achieved some progress, but the quantitative detection of cracks continues to encounter challenges. Firstly, pavement cracks exhibit a wide range of dimensional variations and often manifest in variable, complex backgrounds, posing difficulties for deep learning-based target detection algorithms. Secondly, in instances in which a photo contains multiple or exceedingly long cracks, using a single measured distance as the representative shooting distance for width calculation lacks precision. This paper introduces a novel crack-labeling method that employs a small frame overlay technique to accurately label and train a crack dataset. Additionally, a binocular ranging algorithm is used to determine the shooting distances to various crack segments. By precisely measuring the shooting depth, this approach significantly enhances the accuracy of crack width measurements, allowing for differentiated width calculations across various crack sections.