Industrial surface defect inspection is important for maintaining product quality, but reliable automatedinspection remains challenging. Defects can be small, subtle, and diverse, and their visual appearancecan be affected by complex surface backgrounds, lighting conditions, and viewpoint changes. In addi-tion, industrial datasets often contain limited labeled defect samples, which makes it difficult to traintask-specific deep learning models from scratch, especially when new products or production conditionsrequire model adaptation. This thesis evaluates the practical potential of pretrained foundation models(FMs) for industrial surface defect inspection, focusing on classification, object detection, and stabilityunder image changes. The study used the GC10-DET dataset and evaluated three pretrained foundationmodels: Contrastive Language–Image Pre-training (CLIP) as a prompt-based vision-language model forzero-shot defect classification, Grounding DINO as a text-guided object detector for zero-shot object de-tection, and DINOv3 as a self-supervised vision foundation model. DINOv3 was used as a pretrainedvisual feature extractor for classification and as the backbone in a Faster R-CNN detector to evaluatehow fine-tuning affected object detection performance. Model stability was assessed under geometrictransformations and changes in brightness and contrast. CLIP showed limited performance for defectclassification and was sensitive to prompt wording, although it performed better in the defective ver-sus non-defective prompt comparison than in identifying the exact defect type. DINOv3 features per-formed well for classification when combined with a lightweight classifier. Grounding DINO showedlimited zero-shot localization performance. In the DINOv3 object detection experiments, performancevaried with the fine-tuning strategy and the way the pretrained features were integrated into the detector.The stability results indicated smaller changes under horizontal flips and larger changes under rotations.Taken together, the findings suggest that foundation models can provide useful starting points for indus-trial inspection, but further evaluation is needed to understand how well these models transfer to differentindustrial conditions.