AGRF-Net:基于边界感知与自适应门控残差融合的RGB-D语义分割网络OA

The reliability of environment perception systems is the foundation upon which autonomous driving technology highly depends.However,in complex road scenes,single-modal perception struggles to simultaneously address the detection challenges posed by drastic illumination changes and the pres-ence of both positive and negative obstacles.To improve the accuracy and robustness of cross-modal fea-ture fusion,this paper designs an adaptive gated residual fusion network(AGRF-Net).In this network,auxiliary edge information and multi-dimensional adaptive gating mechanisms are utilized to optimize the complementarity and integration process of cross-modal features.First,an edge-aware input enhancement mechanism is introduced to extract auxiliary edge features,and the network's perception capability of the fine geometric contours of obstacles is strengthened through a multi-scale gated edge fusion module.Second,a depth multi-level feature enhancement module is utilized to perform internal refinement and contextual enhancement of depth features,thereby suppressing the inherent noise of the depth modality and supplement geometric details.Third,a cross-modal residual gating network is constructed as the core fusion module,which filters effective depth information through multi-scale contextual reliability gating and achieves high-order feature integration by combining a cross-modal attention mechanism.Finally,ex-perimental results validate the feasibility and accuracy of the proposed strategy in handling the collabora-tive detection of both positive and negative obstacles in complex road environments.