## 1 Introduction
This design integrates several efficient modules, including Squeeze and Expand (SE), Multi-Scale Convolution (MC), and depthwise separable convolution (DSC). The SE module adaptively modulates channel-wise weights to enhance feature representation, the MC module extracts image features at various scales, and the DSC reduces both parameter count and computational load, thus improving overall network efficiency. This architecture ensures high detection accuracy while significantly lowering complexity and resource demands, making it well-suited for deployment in real-world, resource-constrained environments. [PERSON] et al. proposed an innovative neural network architecture named the Selective Feature Fusion and Irregular Perception Network for pavement crack detection [10], which selectively fuses multi-level features, regulates the transmission of salient information at different stages, and effectively models irregular crack structures. Nonetheless, these methods still exhibit certain limitations, such as high reliance on domain expertise, the need for separate optimization of feature extraction and classification processes, complex preprocessing steps, and relatively limited generalization ability. With its successful development and growing adoption, deep learning technology has been increasingly applied to pavement damage detection and classification. Due to the unique properties of pavement damage images such as high resolution, a low proportion of damaged areas, and the limitations in enabling end-to-end training. [PERSON] et al. proposed a series of straightforward yet effective end-to-end deep learning approaches named WSPLINs [11]. This framework initially segments pavement images into patches at multiple scales using different acquisition strategies, and subsequently employs a patch label inference network (PLIN) to predict labels for these patches. In another study, [PERSON] et al. [12] developed an automated pavement crack detection technique based on structured prediction utilizing a CNN [13; 14; 15; 16]. This is a supervised algorithm based on deep learning, where the CNN learns and trains on raw pavement crack images, modeling crack detection as a multi-class label problem. This approach achieves effective pavement damage crack classification and detection without requiring additional preprocessing steps, showing certain performance improvements. While these methods have shown some effectiveness in applying deep learning to pavement damage detection and classification, they still have certain shortcomings. For instance, most of these methods involve deep learning models wherein pavement damage detection and classification are treated as simple object detection or image classification problems, with relatively little consideration for the subtle features of pavement damage images [17]. Pavement images often exhibit high similarity between normal and damaged areas. Specially, normal images and damaged images differ only slightly in the damaged areas, which occupy a small proportion of the entire pavement image. In addition, different types of cracks (such as alligator cracks, transverse cracks, and longitudinal cracks) have high similarity, making it difficulty to categorize the damage accurately. To overcome the mentioned problems, this paper proposes a novel method named fine feature extraction and attention re-embedding (FFEAR) based on the Swin Transformer for pavement damage detection and classification. Overall, FFEAR surpasses the original Swin Transformer and offers a specialized approach for pavement damage detection and classification. Firstly, we developed a specialized model, FFEAR, based on the CQU-BPDD pavement damage dataset. Building on the Swin Transformer architecture, this model is specifically optimized for pavement damage detection and classification. To tackle challenges unique to pavement images such as low damage visibility, high similarity between crack types, and uneven lighting we introduced a multi-head self-attention feature re-embedding mechanism.