## 3 Results and Discussion
### Ablation Study  
To reveal the effectiveness of the different improvement methods in ISTD-DisNet, the MAM and TCUM modules and the mixed loss function (\(\lambda_{1}=0.7\), \(\lambda_{2}=0.3\)) were replaced or removed, respectively. Table 3 shows that, as far as the effect of a single improvement  
Figure 6: Segmentation results comparison for the seven classes of distress and distractors in complex scenes by the eight methods on the ISTD-PDS7. The red box is the focus area. measure on the distress segmentation performance is concerned, the mixed loss function, which takes into account the positive and negative sample imbalance, contributes the most to the improvement in the _mIoU_. Compared with the baseline network, the model improves the \(F1\) score and _mIoU_ by 0.93% and 1.58%, respectively, without increasing the number of parameters, which suggests that the improvement strategy that focuses on the positive and negative sample imbalance is more effective for the pavement distress segmentation task. This is followed by the MAM module, and the smallest contribution is made by adding the learnable uploading module at the decoding stage (TCUM). The combination of TCUM and Mixed_Loss resulted in the greatest improvement in the model's segmentation performance when the two improved strategies were used in combination; the simultaneous use of the three improvement measures can improve the \(F1\) score and _mIoU_ of SegFormer by 1.28% and 2.18%, respectively. The results of the ablation experiments show that the three improvement measures in ISTD-DisNet can effectively improve the performance of pavement distress detection. With regard to the accuracy, computational complexity, number of parameters, and FPS, ISTD-DisNet has the highest accuracy and fewer parameters. In addition, to more visually reveal the effects of the MAM and TCUM modules on the model's segmentation performance, we randomly selected different distress samples from the ISTD-PDS7 test set and used GradCAM to visually represent the feature maps of the TCUM module output locations in Figure 1, i.e., the output features of the MLP layer in the tail of the original SegFormer. GradCAM is a popular visualization method that takes gradient-weighted class-activation mappings in the form of heatmaps to be a visual representation and provides interpretability without compromising the accuracy. Darker areas in the visualization results indicate higher model responsiveness to the target class. The results are shown in Figure 7, where the first column contains the original images of the seven different distress categories; the second column contains the labels of the corresponding distress types; the third column contains the heatmaps of the corresponding locations of SegFormer; the fourth column contains the heatmaps of the corresponding locations after adding only the MAM module; the fifth column contains the heatmaps of the corresponding locations after adding only the TCUM module; and the sixth column contains the heatmaps of the corresponding locations for ISTD-DisNet. Observation of columns 3 and 4 reveals that the addition of the MAM module improves the model's response to the distressed areas and enhances the completeness of the predictions. In particular, for the cement cracks with expansion joints in the background (four rows), the MAM module is also effective in reducing the false-positive predictions of the model. Observation of columns 3 and 5 reveals that the addition of the TCUM module proposed in this article improves the model's ability to restore the details of the damage and solves the problem of the insufficient fine-grained segmentation of the baseline model. It should be noted that, when only the TCUM module is used, it leads to the incomplete extraction of some cracks and pits (two rows and five columns, two rows and seven columns). Observing columns 3 and 6, it can be seen that the simultaneous use of the MAM and TCUM modules