Year: 2026
Type: data-paper
Abstract: We developed and released an image dataset for surface defect detection on waterborne painted wood products. The dataset comprises 13400 high-resolution images capturing four defect types: scratches, cracks, bubbles, and holes. These include 3645 bubble defects, 3498 scratch defects, 3256 crack defects, and 3001 hole defects. Data were acquired in an operational production facility in Jiangshan, China, using specialized industrial cameras. Professional annotators performed rigorous labeling to ensure accuracy. This dataset provides critical data support for deploying deep learning models in real-world industrial assembly lines. Researchers may leverage this dataset to develop automated machine learning solutions for multi-class defect detection. Such techniques enable timely defect detection and remediation, ensuring finish integrity and final product quality.
Source: Scientific Data
Authors: Na Jia, Kai Chen, Cheng Liu, NanChao Wang
Institution: Northeast Forestry University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7123649245