Abstract
Hardwood flooring products are popular construction materials because of their aesthetics, durability, low maintenance requirements, and affordability. To ensure product quality during manufacturing, common defects such as cracks, chips, or stains are typically detected and classified manually, but this process can decrease productivity. The aim of this study was to develop an automatic machine vision-based inspection system with a robust algorithm for inspecting small hardwood flooring defects in a production line. This defect-inspection algorithm is based on image-processing techniques, including background elimination, boundary approximation, and defect inspection of photographs. The YOLOv5 deep-learning algorithm for object detection was applied to detect surface defects. The resulting algorithm identified the quality of each specimen (i.e., either good or defective). The influences of colour and surface patterns on defect inspection were experimentally investigated under light conditions. The algorithm was adaptable to specimens with different colours and patterns under various conditions, demonstrating the potential of this approach in practical situations.
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