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Visual quality inspection systems, crucial in sectors like manufacturing andlogistics, employ computer vision and machine learning for precise, rapiddefect detection. However, their unexplained nature can hinder trust, erroridentification, and system improvement. This paper presents a framework tobolster visual quality inspection by using CAM-based explanations to refinesemantic segmentation models. Our approach consists of 1) Model Training, 2)XAI-based Model Explanation, 3) XAI Evaluation, and 4) Annotation Augmentationfor Model Enhancement, informed by explanations and expert insights.Evaluations show XAI-enhanced models surpass original DeepLabv3-ResNet101models, especially in intricate object segmentation.

XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection