## 3 Results  
In this section, we present an empirical evaluation of our proposed model and compare its performance with six contemporary, state-of-the-art methods in image classification. The primary aim of this study was to ascertain the performance and efficiency of our model in addressing image classification tasks with a particular focus on pavement distress categories. A dataset embodying an array of pavement distress categories was meticulously analyzed as part of the evaluation process. Extensive experimentation underscored the superior performance of our sparse model in terms of classification accuracy as compared to the other methods. * ResNet (Residual Network) [13]: A type of convolutional neural network (CNN) that uses shortcut connections (also known as residual connections) to make deeper models possible. These shortcut connections help to solve the vanishing gradient problem, which can occur when training very deep neural networks. ResNet models can have hundreds or even thousands of layers, and they have achieved state-of-the-art performance on many computer vision tasks. * VGGNet (Visual Geometry Group Network) [2]: The model is characterized by its simplicity, using only 3 \(\times\) 3 convolutional layers stacked on top of each other in increasing depth. The depth of the network, which is a defining characteristic of VGGNet, contributes to learning more complex features at higher layers. Despite its simplicity and depth, it is quite resource-intensive. * DenseNet (Densely Connected Convolutional Networks) [14]: This is a type of CNN that connects each layer to every other layer in a feed-forward fashion, different from ResNet which only involves a connection from the previous layer. This architecture enhances the vanishing gradient problem, strengthens feature propagation, encourages feature reuse, and significantly reduces the number of parameters, thus making the model more efficient. * EfficientNet [15]: This model uses a simple yet effective compound coefficient. The EfficientNet models, with this compound scaling method, have achieved state-of-the-art accuracy on ImageNet while being up to more efficient than ConvNets. * HRNet (High-Resolution Networks) [16]: The HRNet maintains high-resolution representations throughout the entire network, different from other networks that might reduce the resolution in the earlier stages. It has parallel multi-resolution convolutions which exchange information across different resolutions, making it particularly effective for semantic segmentation tasks where high-resolution spatial information is important. * RexNet [12]: This model proposes a simple yet effective channel configuration that can be parameterized by the layer index. As a result, RexNet achieves remarkable performance on ImageNet classification and transfer learning tasks including COCO object detection, COCO instance segmentation, and fine-grained classifications. * SparseModule refers to the ablation experiment of our method, using only SparseModule without adjusting the loss function. **Dataset. ** Our dataset comprises over 100,000 km of road data from various Chinese regions, such as Jiangsu, Henan, Xinjiang, and Shanxi, and features a variety of pavement conditions. Data were collected using a 3D line laser scanner mounted on a vehicle, with a 1 mm sampling accuracy and a 100 km/h acquisition speed, scanning a 4 m wide lane at a 28 kHz frequency. The raw data were converted to images, and the brightness was homogenized. Images were resized to 512 \(\times\) 512 pixels for model input as shown in Figure 5. Road engineering professionals performed pixel-level mask labeling, and domain experts confirmed accuracy. The dataset was split into 80% training, 10% validation, and 10% test sets. The Road dataset includes diverse cracks and healthy pavement, with no overlap or excess images from the same road segment, ensuring a high-resolution and robust dataset. **Metrics.