###### Abstract
The validation results showed that the trained network model achieved an overall detection and classification accuracy of 84.9%, considering both the crack type and severity level. [PERSON] et al. [11] proposed an improved model called C-Mask RCNN, which enhances the quality of crack region proposal generation through cascading multi-threshold detectors. The experimental results indicated that the mean average precision of the C-Mask RCNN model's detection component was 95.4%, surpassing the conventional model by 9.7%. [PERSON] et al. [12] also proposed a crack detection method based on an improved Faster-RCNN for small cracks in asphalt pavements, even under complex backgrounds. The experiments demonstrated that the improved Faster-RCNN model achieved a detection accuracy of 85.64%. [PERSON] et al. [13] conducted experiments to evaluate the effectiveness of Faster R-CNN and Mask R-CNN and compared their performances in different scenarios. The results showed that Faster R-CNN exhibited a superior crack detection accuracy compared to Mask R-CNN, while both models demonstrated efficiency in completing the detection task with small training datasets. The study focuses on comparing Faster R-CNN and Mask R-CNN, but does not compare the proposed methods with other existing crack detection methods. In general, these above-mentioned methods not only detect the category of an object, but also determine the object's location in the image [14]. The use of deep learning methods can reduce labor costs and improve work efficiency and intelligence in recognizing road cracks [1]. Meanwhile, unmanned aerial vehicles (UAV) have demonstrated their versatility in a wide range of applications, including urban road inspections. This is attributed to their exceptional maneuverability, extensive coverage, and cost effectiveness [2]. Equipped with high-resolution cameras and various sensors, these vehicles can capture images of the road surface from multiple angles and heights, providing a comprehensive assessment of its condition. Several researchers have utilized UAV imagery to study deep learning methods for road crack object detection, and they have achieved impressive accuracy results. [PERSON] et al. [15] proposed an automatic crack detection technique using artificial neural networks. The study focused on classifying cracks and non-cracks, and the algorithm achieved a success rate of 79.9%. [PERSON] et al. [2] utilized images collected by a UAV to conduct experimental comparisons of three deep learning target detection methods (Faster R-CNN, YOLOv3, and YOLOv4) via convolutional neural networks (CNN). The study verified that the YOLOv3 algorithm is optimal, with an accuracy of 56.6% mAP. In another study, [PERSON] et al. [16] proposed an RDD-YOLOv5 algorithm with Self-Attention for UAV road crack detection, which significantly improved the accuracy with an mAP of 91.48%. Furthermore, [PERSON] et al. [17] proposed an improved YOLO3 algorithm for road damage detection from UAV imagery, incorporating a multi-layer attention mechanism. This enhancement resulted in an improved detection accuracy with an mAP of 68.75%. [PERSON] et al. [1] utilized the YOLOv4 deep learning network and evaluated its performance using various metrics such as F1-score, precision, recall, mAP, and IoU. The results showed that the proposed model had an acceptable performance in road crack recognition. Additionally, [PERSON] et al. [18] introduced a UAV visual inspection method based on deep learning and image segmentation for detecting cracks on crane surfaces. Moreover, [PERSON] et al. [19] presented a lightweight UAV road crack detection algorithm called GC-YOLOv5s, which achieved an accuracy validation mAP of 74.3%, outperforming the original YOLOv5 by 8.2%. [PERSON] et al. [20] introduced BL-YOLOv8, an improved road defect detection model that enhances the accuracy of detecting road defects compared to the original YOLOv8 model.