## 2 Method
### Sparse Module  
The second key component of our method is the Sparse Module. This module serves a critical role in our proposed model. It is crafted to focus on the sparse targets amidst the monotonous backgrounds, a feature often overlooked in conventional methods. The module is designed to be adaptable and scalable, ensuring compatibility with diverse model architectures. It harnesses the power of sparse representation theory, integrating sparse paradigm formulas to enhance the extraction of sparse features from pavement images. Figure 2 representation portrays the comprehensive architecture of our model, meticulously designed to address the challenges of pavement distress classification. This schematic illustrates a holistic workflow that initiates with an input pavement image and ends with the extraction and sharing of sparse image features then getting classification results. The pavement image serves as the initial input, and the image is processed through four distinctive feature extractors. Each of these extractors is unique in terms of size, offering a multi-scale method to feature extraction. Simultaneously, as part of this intricate process, we introduce a bespoke sparse module. This component is designed to extract features from the largest size features, further enhancing the ability of the model to identify  
Figure 2: The overall architecture of our model. Given a pavement image, four feature extractors of different sizes extract image features and iterate forwards, sharing information between the different sizes. Meanwhile, we design a sparse module to take features from the max size features, while using a \(1\times 1\) convolution to keep the features, a pooling layer to read the sparse points and expand the sensory field using two dilated convolutions with different sizes, and finally concatenate the outputs up to participate in the sharing of different sizes as sparse features. sparse areas of interest. The utilization of a \(1\times 1\) convolution aids in the maintenance of these features. In the following step, a pooling layer is employed to identify and highlight sparse points within the image, consequently expanding the sensory field of the model. The sparse module also leverages dilated convolutions of varying sizes to further scrutinize the image, focusing on the comprehensive coverage and extraction of relevant features. The model then amalgamates these diverse outputs, effectively compiling them into a singular, coherent format. This concatenated output serves to participate in multi-dimensional information exchange among different sizes, thus enhancing the overall feature extraction process. Figure 3 visually encapsulates a comparative exploration of feature extraction methodologies for pavement distress. The graphical representation utilizes color coding to distinguish between different regions of interest within the pavement image. Notably, the red box delineates the target area, indicative of pavement distress, while the blue box demarcates the surrounding background region. The crux of our enhancement lies in the ingenious alteration of the conventional feature extraction process. As the illustration suggests, we have enhanced the qualitative aspect of feature extraction by expanding the receptive field. This expansion allows the model to grasp a broader perspective of the image, effectively incorporating a larger, more diverse set of contextual details. By doing so, the model transcends the limitations of a narrow field, gaining a more holistic understanding of the spatial and relational intricacies embedded within the image. Furthermore, the illustration elucidates our decision to implement a sparse threshold set at 0.1. This sparse threshold plays a pivotal role in fine-tuning the feature extraction process, ensuring that the model maintains a focus on areas with significant information density.