Deep learning based edge segmentation and classification for plasterboard defect detection
Abstract
This study presents a two-stage framework for binary plasterboard quality inspection that combines edge-region-of-interest (ROI) segmentation with lightweight classification. A production-line dataset of 5,557 images was acquired using an operator-triggered single-board-computer imaging system. On the 916-image manually masked subset, TransAttUNet produced higher ROI-overlap and boundary metrics than U-Net. The proposed MobileNetV2-MobileViT classifier contains 3.91 million parameters and was evaluated on a held-out set of 838 images, achieving 97.37% overall accuracy and 96.99% balanced accuracy. It achieved the highest accuracy among the evaluated transfer-learning baselines under the shared test protocol. Raspberry Pi 4 was used for image acquisition only; on-device inference, latency, memory, and power were not evaluated. Accordingly, the system should be interpreted as a compact, deployment-oriented framework that requires device-level validation before claims of real-time operation can be made.
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BibTeX
@article{teke2026deep,
title = {{Deep learning based edge segmentation and classification for plasterboard defect detection}},
author = {Mustafa Teke and Mehmet Demir},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76925-8},
url = {https://www.nature.com/articles/s41598-026-76925-8},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Deep learning based edge segmentation and classification for plasterboard defect detection
AU - Mustafa Teke
AU - Mehmet Demir
PY - 2026
DA - 2026/10/20
JO - Scientific Reports
DO - 10.1038/s41598-026-76925-8
UR - https://www.nature.com/articles/s41598-026-76925-8
AB - This study presents a two-stage framework for binary plasterboard quality inspection that combines edge-region-of-interest (ROI) segmentation with lightweight classification. A production-line dataset of 5,557 images was acquired using an operator-triggered single-board-computer imaging system. On the 916-image manually masked subset, TransAttUNet produced higher ROI-overlap and boundary metrics than U-Net. The proposed MobileNetV2-MobileViT classifier contains 3.91 million parameters and was evaluated on a held-out set of 838 images, achieving 97.37% overall accuracy and 96.99% balanced accuracy. It achieved the highest accuracy among the evaluated transfer-learning baselines under the shared test protocol. Raspberry Pi 4 was used for image acquisition only; on-device inference, latency, memory, and power were not evaluated. Accordingly, the system should be interpreted as a compact, deployment-oriented framework that requires device-level validation before claims of real-time operation can be made.
SN - 2045-2322
ER -