## 3 Results and Discussion  
### Implementation Details  
* Parameter settings: We implemented the evaluation networks using the publicly available PyTorch.7.0, which is well-known in this community. To improve the learning performance, we adopted the migration-learning method to train the proposed model. The pre-trained model selected the optimal weights of the backbone network trained on the Cityscapes dataset. The model training and testing were performed on a Windows 10 system using Python 3.6. The model training was divided into a freezing phase (50 EP) and a thawing phase (100 EP). The batch size of the freezing training phase was set to 4 and the learning rate was set to 1 \(\times\) 10\({}^{-4}\). The batch size of the unfreezing process was set to 2 and the learning rate was set to 1 \(\times\) 10\({}^{-5}\). The momentum and weight decay were set to 0.9 and 0, respectively. The model-training process was optimized using the AdamW optimizer with a learning rate of 1 \(\times\) 10\({}^{-4}\). All the experiments described in this article were performed using a single GeForce RTX 3090 GPU. * Datasets: To reveal the superiority of ISTD-DisNet, we choose the ISTD-PDS7 [20] benchmark dataset for the model training and testing. The original images in the ISTD-PDS7 dataset were acquired using a mobile acquisition vehicle. This dataset covers seven types of common pavement distress, with different scales for the different distress types. In addition, this dataset has high scene complexity and labeling fineness and contains a sufficient number of negative samples with textural similarity noise, such as shadows, water or oil stains, dropped objects, pavement appendages, etc. ISTD-PDS7 contains 6553 sample images of lesions and 11,974 negative sample images with interference noise. The size of the dataset after data enhancement (vertical flip, horizontal flip, flip, and transpose) was 30,475. The ratio of the ISTD-TR training set to the ISTD-VD validation set was 9:1. ISTD-TE (1000) and ISTD-CRTE (550) were used as the test sets to evaluate the performance in the multi-type distress task and crack segmentation task, respectively, in complex scenarios. Figure 5 shows examples of seven types of distress and negative samples in different scenarios, with the first row showing the original images and the second row showing the corresponding labeled images. Finally, we evaluated the generalizability of the ISTD-DisNet model using the CRACK500 [34], CFD [35], AigleRN [36], and GAPs384 [34] datasets. * Comparison algorithms: In this study, we evaluated the performance of ISTD-DisNet and seven mainstream semantic segmentation models on the ISTD-PDS7 dataset. The comparison algorithms included five CNN-based segmentation networks (SegNet [57], PSPNet [58], DeepLabv3+ [59], U-Net [31], HRNet [60]) and two transformer-based segmentation networks (Swin-Unet [42], SegFormer [44]). In addition, ablation experiments were performed to verify the effectiveness of the improved methods at three locations. * Evaluation metrics: To provide a comprehensive evaluation, the \(F1\) score and the mean intersection over union (_mIoU_) were used to quantitatively evaluate the performance of the different segmentation models. Specifically, the \(F1\) score is the weighted harmonic average, which was used to measure the comprehensive performance of the model. The _IoU_ is the ratio between the intersection and concatenation between the predicted results and the true labels, which measured the segmentation ability of the model in terms of the degree of overlap between the predicted and actual distress. _mIoU_ is the average of the _IoU_. For each image, the _Precision_ and _Recall_ can be calculated by comparing the detected distress with the human-annotated ground truth.