## 1 Introduction  
Pavement damage detection and classification are complex and challenging tasks in road maintenance engineering. With the widespread use of asphalt pavements and the construction of highways, this task has become increasingly important, significantly impacting the safe driving of motorists. Early pavement damage detection and classification primarily relied on manual inspection by professional personnel, who used empirical knowledge to conduct on-site measurements and visual inspections. This manual approach was inefficient and required substantial human effort, with results largely influenced by subjective judgment, causing inconsistencies in detection quality and elevated labor expenses. As a result, it was impractical for large-scale pavement detection [1]. With the development of image technology, the detection process evolved from fully manual to semi-automated. This semi-automated method utilizes high-speed downward-facing digital cameras installed on dedicated data collection vehicles to acquire pavement imagery [2]. For example, France's GERPHO road detection system [3] uses a 35 mm film GPS locator on a data collection vehicle to gather images of road surfaces. Professional staff then use indoor equipment to assess road damage and establish a database, thus moving away from traditional manual methods and reducing the waste of human and traffic resources in maintenance inspections. Similarly, Japan's Komatsu road detection system [4] and Sweden's PAVUE detection vehicle [5] can perform semi-automated detection. While these methods save time by eliminating the need for on-site inspections, they still require human resources to analyze the collected digital images. This process is time-consuming, relies on the expertise of professional inspectors, consumes significant human resources, and remains inefficient, making it unsuitable for large-scale pavement detection. With advancements in computer technology and its widespread adoption, digital image processing has emerged as an efficient method for pavement damage detection and classification, saving both human and material resources. The current development trend focuses on automating and mechanizing this process by employing machine learning, deep learning, and pattern recognition techniques to enhance its adaptability and robustness. Early intelligent detection technologies primarily relied on image processing techniques, manual feature extraction, and conventional classifiers to transform, process, and analyze digital images for pavement damage classification. Typically, image preprocessing is performed using conventional image processing techniques, followed by the application of dedicated feature extraction algorithms and classifiers to identify the presence of cracks and categorize their types, thereby achieving pavement damage classification detection. For instance, [PERSON] et al. presented a method utilizing moment invariants combined with neural networks to process pavement images [6], demonstrating the practicality of this combination in distinguishing various crack types. This technique involves calculating the moment invariants corresponding to different pavement crack forms to extract relevant features, which are then input into a neural network for classification. Likewise, [PERSON] et al. proposed an expert system that integrates wavelet transform with a Radon neural network for pavement distress classification [7]. This system enhances the performance of scale-invariant feature extraction by computing the wavelet modulus, followed by transformation through the Radon neural network, with the resulting feature peaks and parameters used for training and testing. [PERSON] et al. developed a model based on the Mask R-CNN which was effectively applied to road crack detection and hazard identification [8]. [PERSON] et al. introduced a lightweight GAN architecture [9], aimed at achieving high computational efficiency with low processing cost for automated pavement damage detection.