TopoPIS: Topology-constrained pipe instance segmentation via adaptive curvature convolution

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI:10.1016/j.engappai.2024.109547
Jia Hu, Jianhua Liu, Shaoli Liu
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Abstract

Precise and fast pipe instance segmentation is a critical component in industrial automatic assembly, facilitating accurate object detection and pose estimation, optimizing and supervising the assembly process. However, this problem is challenging due to topological errors on fine-scale structures caused by the pipes being complex and slender. To address these challenges, we propose a topology-constrained pipe instance segmentation network (TopoPIS) for complex stacking scene to achieve accurate segmentation with topological correctness. To better extract the features of complex and variable morphological pipes, adaptive curvature convolution is introduced to dynamically adapt to the slender pipe structure and capture critical features. To handle topological errors like broken connections, we propose a novel topological constraint loss function based on persistent homology, which greatly improves the topological continuity of the segmentation. Experimental results on real-world and unseen datasets demonstrate that our TopoPIS outperforms other methods regrading segmentation accuracy and topological continuity.
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TopoPIS:通过自适应曲率卷积进行拓扑约束管道实例分割
精确而快速的管道实例分割是工业自动装配中的一个关键组成部分,有助于准确的对象检测和姿态估计,优化和监督装配过程。然而,由于管道复杂而纤细,在精细结构上存在拓扑误差,因此这一问题具有挑战性。为了应对这些挑战,我们提出了一种针对复杂堆叠场景的拓扑约束管道实例分割网络(TopoPIS),以实现具有拓扑正确性的精确分割。为了更好地提取形态复杂多变的管道特征,我们引入了自适应曲率卷积,以动态适应细长的管道结构并捕捉关键特征。为了处理断开连接等拓扑错误,我们提出了一种基于持久同源性的新型拓扑约束损失函数,大大提高了分割的拓扑连续性。在真实世界和未知数据集上的实验结果表明,在分割准确性和拓扑连续性方面,我们的 TopoPIS 优于其他方法。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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