# XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection

Source: [XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection](<https://www.sota2.com/research/paper/68fa71d61e209131cea53433>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:03.801Z

Authors: Tobias Clement, Truong Thanh Hung Nguyen, Mohamed Abdelaal, Hung Cao

Published: 2024-01-18T11:26:20.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.09900>)

[PDF](<https://arxiv.org/pdf/2401.09900.pdf>)