Conventional wheeled robots are unable to traverse scientifically
interesting, but dangerous, cave environments. Multi-limbed climbing robot
designs, such as ReachBot, are able to grasp irregular surface features and
execute climbing motions to overcome obstacles, given suitable grasp locations.
To support grasp site identification, we present a method for detecting rock
cracks and edges, the SKeleton Intersection Loss (SKIL). SKIL is a loss
designed for thin object segmentation that leverages the skeleton of the label.
A dataset of rock face images was collected, manually annotated, and augmented
with generated data. A new group of metrics, LineAcc, has been proposed for
thin object segmentation such that the impact of the object width on the score
is minimized. In addition, the metric is less sensitive to translation which
can often lead to a score of zero when computing classical metrics such as Dice
on thin objects. Our fine-tuned models outperform previous methods on similar
thin object segmentation tasks such as blood vessel segmentation and show
promise for integration onto a robotic system.

传统的轮式机器人不能穿越科学上有趣但危险的洞穴环境。通过多肢爬行机器人设计（如 ReachBot），我们能够抓住不规则表面特征并执行攀爬动作以克服障碍，前提是有合适的抓取位置。为了支持抓取位置的识别，我们提出了一种检测岩石裂缝和边缘的方法，即 “骨架交叉损失”（SKIL）。SKIL 是一种专为薄物体分割而设计的损失函数，利用标签的骨架结构。我们收集了一组岩石面图像，并进行了手动注释和数据增强。我们还提出了一组新的度量指标 “LineAcc”，用于薄物体分割，以最小化物体宽度对得分的影响。此外，该度量指标对平移不太敏感，这在计算传统的 Dice 等经典度量指标时常常会导致得分为零。我们的精调模型在类似的薄物体分割任务（如血管分割）上优于先前的方法，并展现了与机器人系统的集成的潜力。