# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 5 Conclusions and Future Work  
This paper proposes and adopts a data enhancement strategy based on feature splicing to enhance the NPO++ dataset. By integrating richer negative obstacle features, the model can learn the feature distribution under different lighting conditions and improve its adaptability in complex lighting scenes. Based on the PotCrackSeg model framework, this paper adds an enhanced version of the ASPP module, which improves the ability to capture target boundaries and detail features through a multi-scale receptive field mechanism. In addition, for the feature interaction between RGB and depth modalities, this paper designs an efficient feature fusion module. This module combines adaptive weighting and multi-scale feature alignment technology to achieve cross-modal complementary information fusion. The comparative experimental results show that the method proposed in this paper achieves 70.6 and 83.0 in mIOU and mF1, respectively, which is better than other algorithms. From the ablation experiment, we can see that the various improvements proposed in this paper make different contributions to the improvement of NROSegNet detection performance.  
In addition, we also observe differences in the convergence speed and accuracy performance of different encoders in training. The B2 encoder achieves a better balance between convergence speed and accuracy, which is suitable for scenarios with higher requirements on computational efficiency, while the B4 encoder has an advantage in accuracy, which is suitable for application scenarios with higher requirements on accuracy. In summary, the improvement measures and experimental analyses in this experiment provide new ideas for negative obstacle detection in complex scenarios, which help to improve the reliability and practical application value of the model.  
In future research, we will focus on the following directions to enhance the application potential of NROSegNet. Adaptive learning strategies could be introduced to combine migration learning and incremental learning, enabling better adaptation to different data sources and changing scenarios; more diverse and complex datasets covering a wider range of real-world application scenarios could be built to ensure the performance of the model under various practical conditions, especially in high-uncertainty environments,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **DEP** & **FE** & **MMAF** & **mIoU** & **mFscore** \\ \hline \multirow{4}{*}{NROSegNet} & ✓ & & & 69.5 & 82.2 \\  & & ✓ & & 68.3 & 81.4 \\ \cline{1-1}  & & ✓ & ✓ & 67.9 & 80.5 \\ \cline{1-1}  & ✓ & ✓ & ✓ & 70.6 & 83.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation studies on fusion strategies.  
and further verify its versatility and robustness; model compression and acceleration techniques could be explored to reduce the consumption of computational resources and improve the real-time processing capability of the model.  
Conceptualization, G.H. and C.C.; investigation, Y.L.; methodology, Y.L., W.L. and C.Z.; formal analysis, C.C.; data curation, G.H.; writing--original and draft, Y.L.; writing--review and editing, G.H.; visualization, C.C.; supervision, W.L.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the projects from School-enterprise cooperation of Anhui Polytechnic University under grant [2023 qyhz15] and the Scientific and Technological Project of Quanzhou City under grant [2023C015R].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] and [PERSON] were employed by the companyWuhu HIT Robot Technology Research Institute Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.