提升工业图像检测中物体分割一致性，兼顾精度与速度
Enhancing Shape Perception and Segmentation Consistency for Industrial Image Inspection
- 分离边界与主体信息监督，增强形状感知能力
- 在自建数据集上实现最优分割精度与超50%一致性误差降低
- 适合实时工业检测场景，兼顾高精度与低计算开销
语义分割是计算机视觉的关键研究方向。在工业图像检测中，传统语义分割模型因缺乏对物体轮廓的感知，在不同上下文环境下难以保持固定部件的分割一致性。考虑到工业检测设备的实时性要求和有限算力，亟需高效模型以降低计算复杂度。本文提出一种形状感知高效网络SPENet，通过分别监督图像中边界与主体信息的提取，显著提升分割一致性。SPENet引入可变边界域（VBD）方法，更适配真实场景中的模糊边界描述。同时提出一致性均方误差（CMSE）作为固定部件分割一致性的量化指标。实验表明，该方法在自建数据集上达到最优分割精度，且相较此前表现最佳的实时分割模型，CMSE降低超过50%，兼具优异性能与实时性。
原文摘要 · Abstract (English)
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.
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