用RGB-D相机融合视觉与深度信息，提升机器人识别玻璃的准确率
Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots
- 设计加权特征融合模块，动态结合彩色与深度图像特征
- 在PSPNet上实现7.49%的边界交并比提升，显著增强分割精度
- 提供真实场景数据集，适合服务机器人玻璃检测研究使用
针对服务机器人在复杂环境中通过RGB-D相机识别玻璃表面的挑战，本文提出一种加权特征融合（WFF）模块，动态自适应融合彩色与深度特征，有效应对玻璃的透明、反光和遮挡问题。该模块可作为即插即用组件集成到多种神经网络主干中。同时，构建了MJU-Glass数据集，由服务机器人在真实环境采集，为玻璃分割模型评估提供基准。实验表明，该方法在平均交并比（mIoU）和边界交并比（bIoU）上均有显著提升，与PSPNet结合时，边界交并比提高7.49%，增强了对玻璃边界的识别能力，为降低机器人碰撞风险提供了可靠技术支撑。
原文摘要 · Abstract (English)
We address the problem of glass surface segmentation with an RGB-D camera, with a focus on effectively fusing RGB and depth information. To this end, we propose a Weighted Feature Fusion (WFF) module that dynamically and adaptively combines RGB and depth features to tackle issues such as transparency, reflections, and occlusions. This module can be seamlessly integrated with various deep neural network backbones as a plug-and-play solution. Additionally, we introduce the MJU-Glass dataset, a comprehensive RGB-D dataset collected by a service robot navigating real-world environments, providing a valuable benchmark for evaluating segmentation models. Experimental results show significant improvements in segmentation accuracy and robustness, with the WFF module enhancing performance in both mean Intersection over Union (mIoU) and boundary IoU (bIoU), achieving a 7.49% improvement in bIoU when integrated with PSPNet. The proposed module and dataset provide a robust framework for advancing glass surface segmentation in robotics and reducing the risk of collisions with glass objects.
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