## 3 Results
### Comparison Experiments  
The experiment utilized three images from the UAVID dataset, characterized by paths with multi-scale features and obstructions from buildings, trees, pedestrians, and vehicles. The performance of the proposed methods was evaluated by comparison with four established network models--FCN-8s, FCN-32s, SegNet, and U-Net--through training on the same dataset. The results are visualized in Figure 6. The first row displays the original images, while the subsequent rows from the second to fifth show the outcomes for FCN-8s, FCN-32s, SegNet, and U-Net, respectively. The sixth row features the results from the IndoorPathNet model developed in this study. Areas highlighted in blue indicate where paths were incorrectly classified as background, which is categorized as _FN_. Areas in yellow show where the background was incorrectly classified as paths, which is documented as _FP_. Notable performance disparities exist among FCN, its variants, and the proposed framework in the semantic segmentation task of distinguishing between paths and passable ground. As shown in Figure 6, it is revealed that FCN networks, particularly FCN-8s and FCN-32s, manifest the most pronounced misidentification issues. The improvement observed in FCN-32s over FCN-8s is marginal, often encountering both omission and commission errors in path identification. In contrast, the SegNet network exhibits some enhancement in reducing _FP_ compared to the FCN network, but still struggles to prevent background misidentification as paths. Conversely, the U-Net network demonstrates significant improvement over the SegNet network, displaying a notable reduction in _FN_ and a substantial decrease in _FP_ compared to the FCN network. Although path continuity is enhanced, the detection outcomes remain incomplete. As indicated by the blue numbered boxes (d, e, f, and g) in the first image of different model effect diagrams, the issue of missing road detections improves with the increasing complexity of the models. This suggests that more complex models have a better capacity to capture detailed features. However, this has introduced a new problem, namely the misidentification of buildings becomes increasingly severe, peaking in the U-Net network effect diagram, which indicates that the model has overfitted. By comparing the yellow numbered boxes (1 to 6) between the U-Net network and IndoorPathNet, it can be observed that the misidentification of buildings has been reduced. Thus, all false negatives and false positives are actually concentrated on two issues: missing road detections and the incorrect identification of buildings. The IndoorPathNet proposed in this article achieves a good balance in addressing these two issues. Table 1 presents a detailed numerical comparison of the experiment. Among all models considered in the task scenarios, the proposed model outperforms the other four in terms of _precision_, _recall_, and _IoU_ values. This superior performance underscores the significant advantages of our method over the other network models, particularly in enhancing the accurate identification of path targets, managing occlusion, and tackling discontinuity issues. The predictions generated by our model provide more comprehensive and precise path information. This capability allows for a more complete prediction of paths in small areas and short lengths within the images, thereby demonstrating a more robust path feature extraction ability compared to the other models. Figure 6: An intuitive comparison in the task scenario between current mainstream models and the pro-posed model; (**a–c**) represents different groups. The purple portion represents the segmented path. The rest is identified as the black background. The blue highlighted boxes represent \(FN\), while the yellow ones represent \(FP\). A quantitative comparison was conducted on the entire test set of the UAVID path dataset to further demonstrate the effectiveness of our method in path extraction.