## 1 Introduction  
As road mileage increases, the need for meticulous road health inspections intensifies. Cracks, often the initial indicators of road deterioration, evolve dynamically due to material aging, traffic loading, and environmental factors. In road health monitoring, crack detection is pivotal, offering insights into a road's current state and forecasting potential safety hazards and deterioration patterns. Consequently, profound research into crack detection methodologies and the advancement of precise, efficient crack identification and analysis tools are crucial for intelligent road inspection [1; 2; 3]. The accurate detection, localization, and width measurement of cracks are vital as different pavement conditions necessitate varied repair standards and urgency levels. Current crack detection methods, however, face significant challenges. Traditional manual inspections are labor-intensive, time-consuming, and often inefficient. The complexity and size of road networks further complicate accurate detection and quantitative analysis. In recent years, the development of unmanned aerial vehicle (UAV) technology and deep learning algorithms has opened new avenues for pavement crack detection [4; 5; 6; 7]. UAVs, characterized by their speed, efficiency,compactness, high passability, and low risk, have gained prominence in construction defect detection. When combined with deep learning algorithms, such as convolutional neural networks, they excel in complex detection environments and in hazard identification. These technologies have sparked innovative detection approaches and have been substantiated in numerous studies. Deep learning models, with their intricate network structures, adeptly learn and recognize various crack features from extensive datasets, exhibiting high flexibility and accuracy in identifying diverse crack types and sizes. Compared with traditional manual methods, deep learning substantially enhances the efficiency and speed of crack detection, reduces human resource dependence, and minimizes the likelihood of errors. [PERSON] et al. [8] pioneered the use of deep convolutional neural networks for crack detection and classification. [PERSON] et al. [9] introduced a combination of convolutional neural networks (CNNs) and a sliding window technique for high-resolution crack imagery, advancing structural health detection. [PERSON] et al. [10] employed a novel optimization strategy including deeply differentiable convolution and inverse residual networks to significantly enhance concrete crack detection accuracy. [PERSON] et al. [11] developed the Feature Pyramid and Hierarchical Boosting Network (FPHBN) for crack detection, which layers weights over nested samples to balance simple and complex sample contributions. [PERSON] et al. [12] utilized a Generative Adversarial Network (GAN)-based data enhancement approach for crack images and employed an improved VGG network for enhanced accuracy. [PERSON] et al. [13] designed a CNN-based method using non-overlapping windows to expedite crack detection and streamline analysis. [PERSON] et al. [14] improved the Dempster-Shafer algorithm for handling conflicting CNN results, which boosted detection accuracy. [PERSON] et al. [15] enhanced the VGG16 model and investigated various parameters' impacts on detection outcomes. UAVs are in the spotlight due to their efficiency and flexibility in engineering inspections. They cover extensive areas rapidly and are equipped with high-resolution cameras and sensors for data precision. [PERSON] et al. [16] developed a binocular-vision-based UAV platform for improved image recognition. [PERSON] et al. [17] combined UAV remote sensing with binocular vision for precise power equipment detection. [PERSON] et al. [18] introduced a UAV-mounted binocular vision system for obstacle detection in power infrastructure. [PERSON] et al. [19] utilized a transfer learning approach with a pre-trained VGG-16 network for building crack detection via a UAV. [PERSON] et al.