Segmenting Wood Rot using Computer Vision Models
This addresses a domain-specific problem for the woodworking industry by automating quality control, though it is incremental as it applies existing methods to new data.
The study tackled automating quality assessment of wooden logs by developing an AI model for detecting, quantifying, and localizing defects, achieving an average IoU of 0.71 with performance close to human annotators.
In the woodworking industry, a huge amount of effort has to be invested into the initial quality assessment of the raw material. In this study we present an AI model to detect, quantify and localize defects on wooden logs. This model aims to both automate the quality control process and provide a more consistent and reliable quality assessment. For this purpose a dataset of 1424 sample images of wood logs is created. A total of 5 annotators possessing different levels of expertise is involved in dataset creation. An inter-annotator agreement analysis is conducted to analyze the impact of expertise on the annotation task and to highlight subjective differences in annotator judgement. We explore, train and fine-tune the state-of-the-art InternImage and ONE-PEACE architectures for semantic segmentation. The best model created achieves an average IoU of 0.71, and shows detection and quantification capabilities close to the human annotators.