Recent Advances in Wood Surface Defect Inspection using Deep Learning (2021-2025)

Abstract

Surface inspection plays a critical role in the wood industry, as it helps companies to enhance product quality, improves the utilization of wood resources, and increases the value of final products. In recent years, deep learning has emerged as a promising technique in this domain, offering significant advantages over traditional methods by enabling high-precision, real-time inspection. This paper presents a comprehensive review of advancements in the field from 2021 to 2025. It begins with a brief overview of three foundational aspects: common types of wood defects, publicly available datasets, and evaluation metrics. The core of the review then examines recent deep learning applications, organized according to three computer vision tasks—classification, detection, and segmentation. The paper concludes by discussing key challenges and proposing viable directions for future research, thereby offering a clear technical roadmap.