Clement T, Nguyen Truong Thanh H, Abdelaal M, Cao H (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: Digest of Technical Papers - IEEE International Conference on Consumer Electronics
ISBN: 9798350324136
DOI: 10.1109/ICCE59016.2024.10444225
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.
APA:
Clement, T., Nguyen Truong Thanh, H., Abdelaal, M., & Cao, H. (2024). XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection. In Digest of Technical Papers - IEEE International Conference on Consumer Electronics. Las Vegas, NV, US: Institute of Electrical and Electronics Engineers Inc..
MLA:
Clement, Tobias, et al. "XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection." Proceedings of the 2024 IEEE International Conference on Consumer Electronics, ICCE 2024, Las Vegas, NV Institute of Electrical and Electronics Engineers Inc., 2024.
BibTeX: Download