LoRA fine-tuned Qwen2.5-VL large model for accurate description and location of steel surface defects

In industrial manufacturing, accurate and efficient identification of product surface defects is essential for ensuring product quality, optimizing the production process and reducing cost. However, complex and diverse defect morphologies and the need for fine-grained description present significant challenges. General image description methods based on large visual language models often struggle to provide accurate defect type and location information for specific areas such as steel surface defect recognition. To address this, a defect identification method for the Qwen2.5-VL-3B large model based on LoRA fine-tuning is proposed. We built a specialized dataset covering six key steel surface defectscracks, impurities, plaques, pitting, scale penetration, and scratchesand refined the model through efficient low-rank adaptation. Experimental results demonstrate that the fine-tuned Qwen2.5-VL-3B model significantly improves industrial defect recognition, accurately identifying defect types and locations, thus overcoming limitations of general large models and providing an efficient solution for industrial inspection.