# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
###### Abstract  
To address the issues of low recognition rates and poor detection accuracy for road negative obstacles caused by insufficient feature representation, we propose a novel detection framework: the Negative Road Obstacles Segmentation Network (NROSegNet). The detection performance of the algorithm is improved through a data enhancement strategy based on feature splicing and an adaptive feature enhancement module. Specifically, the data augmentation strategy introduces negative obstacle features into other datasets through geometric transformations and random splicing, effectively increasing the diversity of training data. This can solve the problem of an uneven distribution of data features while improving the performance of the model in capturing illumination changes and local details. The framework further adopts a dynamic multi-scale feature enhancement module to improve the perception of local details and global semantics. A robust multimodal data fusion mechanism and edge-aware optimization strategy are designed to effectively alleviate the problems of noise interference and boundary blur. The experimental results show that the NROSegNet proposed in this paper achieves 70.6 and 83.0 in mIoU and mF1, respectively, which is 2.8 percentage points and 2.9 percentage points higher than other methods. The results fully demonstrate its excellent performance in precise segmentation and boundary detail processing.  
negative obstacle detection; semantic segmentation; RGB-D fusion; feature enhancement 2020 challenges, our research introduces innovative data enhancement strategies to enrich data features and designs efficient feature extraction modules to capture finer detailed features. We also use the complementary fusion of multimodal features to enhance the accuracy of edge segmentation, improve PotCrackSeg [1], and ultimately significantly improve the overall detection performance of the model. The main contributions of this study can be summarized as follows:  
1. We propose an innovative data augmentation strategy that applies diverse geometric transformations to the dataset. This method enriches the feature distribution of samples and improves the generalization ability of the model.
2. In the design of the decoder, we incorporate an enhanced version of the Atrous Spatial Pyramid Pooling (ASPP) module, which improves the ability to capture target boundaries and detailed features through a multi-scale receptive field mechanism. This design effectively optimizes the segmentation performance of the model.
3. To address the feature interaction between RGB and depth modalities, we design an efficient feature fusion module. This module incorporates adaptive weighting and multi-scale feature alignment techniques to achieve complementary information fusion across modalities. Consequently, the network's edge segmentation accuracy in multimodal scenarios is significantly enhanced.  
Through these innovations, our work offers a novel perspective on negative obstacle detection and lays the groundwork for advancing future research in this field. We anticipate that this study will provide valuable insights and practical solutions for real-world applications in related domains while fostering advancements in intelligent transportation technologies.