# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 4 Results
### Experimental Results
#### 4.3.3 Comparative Experiment  
In this ablation experiment, we removed different modules of the model one by one to evaluate the contribution of each part to the overall performance.  
As shown in Table 2, firstly, the removal of the data enhancement module significantly decreases the performance of the model in diverse scenarios. In particular, the generalization ability of the model is significantly weakened in the face of light changes and noise interference, with the mIoU decreasing to 69.5 and mFl decreasing to 82.2. This result indicates that data enhancement significantly improves the model's adaptive ability in complex environments by enriching the feature representation. After excluding the feature extraction enhancement module alone, the model's ability to recognize negative obstacles is significantly weakened. When dealing with edge regions and negative obstacles with complex shapes, the model's detail segmentation ability decreases significantly, with the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{**Modality**} & \multicolumn{2}{c}{**Pothole**} & \multicolumn{2}{c}{**Crack**} & \multirow{2}{*}{**mIoU**} & \multirow{2}{*}{**mFscore**} \\ \cline{3-3} \cline{5-8}  & & **IoU** & & & & & \\ \hline \multirow{3}{*}{TransUNet} & RGB + Depth & 71.1 & 83.1 & 40.3 & 57.5 & 55.7 & 70.3 \\  & RGB & 73.3 & 84.6 & 42.6 & 59.7 & 57.9 & 72.2 \\  & Depth & 43.4 & 60.5 & 20.0 & 33.4 & 31.7 & 47.0 \\ \hline \multirow{3}{*}{EAEFNet} & RGB + Depth & 76.7 & 86.8 & 50.2 & 66.8 & 63.4 & 76.8 \\  & RGB & 77.3 & 87.2 & 50.0 & 66.7 & 63.6 & 76.9 \\  & Depth & 56.5 & 72.2 & 26.2 & 41.5 & 41.3 & 56.8 \\ \hline \multirow{3}{*}{MAFNet} & RGB + Depth & 69.9 & 82.3 & 26.2 & 41.5 & 48.0 & 61.9 \\  & RGB & 75.3 & 82.7 & 18.4 & 31.1 & 44.5 & 56.9 \\  & Depth & 53.4 & 69.6 & 0.1 & 0.1 & 26.7 & 34.9 \\ \hline \multirow{3}{*}{IncoggSeg} & RGB + Depth & 75.9 & 86.3 & 46.8 & 63.8 & 61.4 & 75.0 \\  & RGB & 72.4 & 84.0 & 42.8 & 60.0 & 57.6 & 82.0 \\  & Depth & 52.7 & 69.0 & 10.8 & 19.4 & 31.7 & 44.2 \\ \hline \multirow{3}{*}{PotCrackSeg} & RGB + Depth & 80.4 & 89.2 & 55.1 & 71.1 & 67.8 & 80.1 \\  & RGB & 80.2 & 89.0 & 54.8 & 70.6 & 67.5 & 79.8 \\ \cline{1-1}  & Depth & 78.6 & 87.7 & 50.2 & 67.3 & 64.4 & 77.5 \\ \hline \multirow{3}{*}{NROSegNet} & RGB + Depth & 83.8 & 92.6 & 57.3 & 73.4 & 70.6 & 83.0 \\ \cline{1-1}  & RGB & 81.9 & 90.1 & 54.5 & 70.3 & 68.2 & 80.2 \\ \cline{1-1}  & Depth & 79.8 & 88.7 & 50.8 & 67.7 & 65.3 & 78.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental comparison results.  
mIoU dropping to 68.3 and mF1 dropping to 81.4. This phenomenon verifies the importance of feature extraction enhancement in capturing fine features and improving edge segmentation accuracy. Finally, after removing the improved fusion module, the synergy between RGB and depth modalities is significantly weakened, and the model has difficulty fusing RGB information and depth information. Especially in the process of heterogeneous information fusion, the overall performance of modality fusion decreases, with the mIoU dropping to 67.9 and mF1 dropping to 80.5. This suggests that the improved fusion module is crucial in the process of multimodal data fusion, which has a direct effect on the model's ability to recognize and segment negative obstacles.  
Based on the above analysis, we can clearly observe the impact of each module on the overall performance of the model and their interconnections. This emphasizes the necessity of each component in building an efficient and robust detection model.