融合注意力机制的铝型材缺陷检测研究OA

In the field of industrial material production and management,traditional detection methods are difficult to meet the diverse detection requirements for surface coating cracks and other defects of aluminum profiles.Moreover,existing detection models have problems of missed detection and false detection of small targets.This paper proposes a surface defect detection method for aluminum profiles based on YOLOv8n-SE.By embedding the SE attention mechanism in the neck network of the YOLOv8n model,the feature extraction ability is enhanced,improving the positioning accuracy and defect sensitivity of the defect area.Experiments were conducted using an aluminum profile defect dataset,and the proposed method was compared with lightweight models such as Faster R-CNN and YOLOv5n combined with different attention mechanisms.The research results show that the average precision(mAP)of the improved model reaches 75.0%,a 4.2%higher than the original YOLOv8n model,with the number of parameters remaining basically unchanged and the inference speed only decreasing by 0.3%.The improved YOLOv8n model with the embedded SE attention mechanism can effectively improve the recognition effect of surface defects of aluminum profiles,solve the problem of missed detection and false detection of small targets,and maintain the efficient inference advantage of lightweight models,making it suitable for the defect detection requirements of aluminum profiles in industrial scenarios.