AI 中文总结
本文提出边缘感知实例分割框架，构建含透明实验器皿的LabGlass-IS数据集，实现机器人对透明器皿的实时避障，在边界F值等指标上优于现有方法，真实机器人试验避障成功率达93.3%。
AI 中文摘要
本文提出一种边缘感知实例分割框架，该框架仅通过视觉感知即可实现机器人对透明实验玻璃器皿的实时避障。透明容器因折射、镜面反射以及缺乏稳定内部纹理，难以用常规方法分割，但其边界轮廓仍是相对可靠的视觉线索。利用这一观察，我们在单阶段实时实例分割骨干网络基础上，添加了轻量级边缘检测分支、边缘引导注意力融合模块以及无参数的SimAM模块，并构建了LabGlass-IS数据集，这是包含3485张图像、21类真实实验玻璃器皿的实例分割数据集。增强后的模型在对比方法中达到了97.80的最高边界F值（Boundary F-score），比YOLO提示的FastSAM框架高出18.93个边界F值点。此外，该模型保持每帧7.1ms的推理速度，且仅为精度最接近的竞争对手模型参数的2.85%。对掩码质心的多视图三角测量进一步提供了保守边界体积碰撞约束的三维位置。真实机器人试验实现了93.3%的避障成功率，表明所提出的从感知到动作的流水线在易碎透明物体间机器人避障中的可行性。我们的代码和视频可在指定URL获取。
英文摘要
This paper presents an edge-aware instance segmentation framework that enables real-time robotic collision avoidance with transparent laboratory glassware using purely visual perception. Transparent vessels defy conventional segmentation due to refraction, specular reflection, and the absence of stable interior texture, yet their boundary contours remain comparatively reliable visual cues. Exploiting this observation, we augment a one-stage real-time instance segmentation backbone with a lightweight edge-detection branch, edge-guided attention fusion, and a parameter-free SimAM module, and further construct LabGlass-IS, a 3485-image, 21-category instance segmentation dataset of real laboratory glassware. The enhanced model achieves the highest Boundary F-score of 97.80 among compared methods, outperforming the YOLO-prompted FastSAM framework by 18.93 BF points. Furthermore, it maintains an inference speed of 7.1ms per frame and requires only 2.85% of the parameters of the closest accuracy competitor. Multi-view triangulation of mask centroids further provides 3D positions for conservative bounding-volume collision constraints. Real-robot trials achieve a 93.3% collision avoidance success rate, indicating the feasibility of the proposed perception-to-action pipeline for robot collision avoidance among fragile transparent objects. Our code is available at https://github.com/havishamy/TransYOLO_3D. Our video is available at https://havishamy.github.io/paper-videos/.
