Abstract
Semantic segmentation stands as a pivotal research focus in computer vision. In the context of industrial image inspection, conventional semantic segmentation models fail to maintain the segmentation consistency of fixed components across varying contextual environments due to a lack of perception of object contours. Given the real-time constraints and limited computing capability of industrial image detection machines, it is also necessary to create efficient models to reduce computational complexity. In this work, a Shape-Aware Efficient Network (SPENet) is proposed, which focuses on the shapes of objects to achieve excellent segmentation consistency by separately supervising the extraction of boundary and body information from images. In SPENet, a novel method is introduced for describing fuzzy boundaries to better adapt to real-world scenarios named Variable Boundary Domain (VBD). Additionally, a new metric, Consistency Mean Square Error(CMSE), is proposed to measure segmentation consistency for fixed components. Our approach attains the best segmentation accuracy and competitive speed on our dataset, showcasing significant advantages in CMSE among numerous state-of-the-art real-time segmentation networks, achieving a reduction of over 50% compared to the previously top-performing models.
Abstract (translated)
语义分割是计算机视觉领域的核心研究方向之一。在工业图像检测的背景下，传统的语义分割模型由于缺乏对物体轮廓的认知，在不同上下文环境中难以保持固定组件的一致性分割结果。鉴于工业图像检测设备实时性和计算能力有限的要求，创建高效的模型以降低计算复杂度也显得尤为重要。 本文提出了一种基于形状感知的高效网络（Shape-Aware Efficient Network, SPENet），该网络通过分别监督图像边界和主体信息的提取来实现优异的分割一致性，并专注于物体形状。在SPENet中引入了一种名为可变边界域（Variable Boundary Domain, VBD）的新方法，用于描述模糊边界以更好地适应现实世界场景。 此外，本文还提出了一项新的度量标准——一致性均方误差(Consistency Mean Square Error, CMSE)，用以衡量固定组件的分割一致性。我们的方法在我们构建的数据集中取得了最佳的分割精度和竞争力的速度，在众多实时语义分割网络中展现了显著的优势，并且在CMSE指标上相比之前性能最优的模型减少了超过50%。
URL
https://arxiv.org/abs/2505.14718
