Automated visual inspection is an important area within industrial manufacturing. Machine learning and computer vision are increasingly used for defect detection. This thesis investigates automated defect detection for treated wooden surfacesin an industrial environment. The motivation for the project is the need for a quality inspection system from an industrial window manufacturing company. Manualinspection is time consuming and subject to inconsistency, which motivates an investigation into a camera based machine learning system that could provide a moreconsistent and cost effective alternative. Existing research has mainly evaluated supervised and unsupervised methods under controlled conditions. This thesis willinstead evaluate the methods under identical production-like conditions on treatedwood surfaces. To address the problem, a prototype vision inspection system wasdeveloped using a 2D camera together with deep learning models for defect detection. A supervised classification model based on ResNet-18 and an unsupervisedanomaly detection model based on PatchCore were implemented and evaluated using both controlled and production-like image data. The results showed that bothmodels achieved relatively strong performance under controlled conditions. However, the production-like evaluation introduced substantially lower precision due to ahigh number of false positive predictions. The anomaly model demonstrated highersensitivity towards deviations and the classification model produced more selectivepredictions. A combined approach reduced false positives while maintaining a reasonable defect detection capability. The results show that combining supervised classification and unsupervised anomaly detection can provide a more balanced inspection strategy, although further development is required to make it a reliable inspectionsystem.