翁静梁,王龙业,曾晓莉,刘梦瑶,易婷

• 1:西南石油大学电气信息学院
• 2:西藏大学信息科学技术学院

摘要(Abstract)：

针对盲道分割任务中背景复杂、边界模糊以及盲道形状多样化导致的分割效果不佳等问题，提出了一种位置强化与双路径边缘增强解码的盲道分割方法。设计了位置强化特征提取模块，通过在降采样过程中融入MobileVitv3构成的主干网络，增强模型对盲道特征的感知能力，充分保留上下文信息；提出了融合通道位置强化注意力模块，分别在通道和空间维度上强化特征提取，提升模型在低对比度场景下有效区分盲道与背景信息的能力；采用双路径边缘增强解码方式，对盲道的区域与边界信息进行解码，并结合联合损失函数进一步优化边界细节的处理。此外，针对当前缺乏大规模公开盲道数据集的问题，自制了一个多场景盲道数据集（MSBD），为模型训练和实验验证提供了更丰富的数据支持。实验结果表明，该网络在MSBD数据集上的mIoU、Precision、Recall以及F1-score分别达到96.82%、96.84%、96.48%、96.66%，均优于SegFormer、Deeplabv3+等网络；在输入图片大小为512×512×3时，参数量和计算量分别为1.73×10~6和1.93 GFLOPs，且推理帧率可达86 FPS，综合性能优于所对比网络；同时该网络在公开盲道人行道数据集（BACD）和Cityscapes数据集上的综合指标也优于所对比网络。

关键词(KeyWords)： 盲道分割;位置强化;注意力模块;多场景;边界

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（62161047）~~

作者(Author): 翁静梁,王龙业,曾晓莉,刘梦瑶,易婷

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