Abstract
Different types of liquids such as water, wine and medicine appear in all aspects of daily life. However, limited attention has been given to the task, hindering the ability of robots to avoid or interact with liquids safely. The segmentation of liquids is difficult because liquids come in diverse appearances and shapes; moreover, they can be both transparent or reflective, taking on arbitrary objects and scenes from the background or surroundings. To take on this challenge, we construct a large-scale dataset of liquids named LQDS consisting of 5000 real-world images annotated into 14 distinct classes, and design a novel liquid detection model named LQDM, which leverages cross-attention between a dedicated boundary branch and the main segmentation branch to enhance segmentation predictions. Extensive experiments demonstrate the effectiveness of LQDM on the test set of LQDS, outperforming state-of-the-art methods and establishing a strong baseline for the semantic segmentation of liquids.
Abstract (translated)
不同类型的液体，如水、酒和药剂，在日常生活的各个方面都会出现。然而，这个领域的问题并未得到足够的关注，这限制了机器人安全地避开或与液体互动的能力。液体质检的难度在于液体外观多样且形态各异；此外，它们可能是透明的或是反光的，并能随背景或周围环境的变化而变化。为了应对这一挑战，我们构建了一个名为LQDS的大规模液体质检数据集，该数据集中包含5000张真实世界的图片并标注为14种不同的类别。同时，我们设计了一种新型液体检测模型LQDM，它利用专用边界分支和主要分割分支之间的交叉注意力机制来增强分割预测的准确性。 通过广泛的实验验证了LQDM在LQDS测试集上的有效性，并且其性能超过了现有的先进技术，为液体质检的语义分割设定了一个强大的基准。
URL
https://arxiv.org/abs/2601.00940
