Enhancing Shape Perception and Segmentation Consistency for Industrial Image Inspection

TL;DR

This paper introduces SPENet, a shape-aware efficient network for industrial image segmentation that improves consistency and accuracy by focusing on object contours and boundary adaptation, suitable for real-time applications.

Contribution

The paper proposes a novel shape-aware network with boundary and body supervision, a fuzzy boundary description method, and a new consistency metric, advancing industrial segmentation performance.

Findings

Achieves superior segmentation accuracy on industrial datasets.

Reduces segmentation inconsistency with the new CMSE metric.

Offers a faster, more efficient model suitable for real-time industrial inspection.

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.