XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Tobias Clement Truong Thanh Hung Nguyen Mohamed Abdelaal Hung Cao
Visual quality inspection systems, crucial in sectors like manufacturing and
logistics, employ computer vision and machine learning for precise, rapid
defect detection. However, their unexplained nature can hinder trust, error
identification, and system improvement. This paper presents a framework to
bolster visual quality inspection by using CAM-based explanations to refine
semantic segmentation models. Our approach consists of 1) Model Training, 2)
XAI-based Model Explanation, 3) XAI Evaluation, and 4) Annotation Augmentation
for Model Enhancement, informed by explanations and expert insights.
Evaluations show XAI-enhanced models surpass original DeepLabv3-ResNet101
models, especially in intricate object segmentation.