Small Target Defect Detection Method on Aluminum Ingot Surface based on Improved YOLOv8n-SimAM

Abstract

In this paper, an intelligent detection solution based on the improved YOLOv8n architecture is proposed to address the key challenges in the detection of surface defects in aluminum ingots. As an important raw material in modern industry, the surface defects of aluminum ingots increase significantly in high-speed production environments. Traditional detection methods have difficulty dealing with the problems of small targets and morphological diversity. This study effectively improves the model's ability to recognize multi-morphological small defects by introducing the parameter-free SimAM three-dimensional attention mechanism. The innovations include: 1) constructing a high-definition aluminum ingot defect dataset based on an actual production line and systematically analyzing the scale distribution characteristics of the defects; 2) integrating the three-dimensional attention mechanism in the network backbone layer to enhance the feature extraction capability of small targets and solve the morphological diversity problem of burrs and slag inclusions; 3) through comparative experimental verification, the improved YOLOv8n-SimAM model achieved a mAP of 64.5% in aluminum ingot surface defect detection, an increase of 9 percentage points over the baseline model, and improved the detection of burrs and slag inclusions by 6.3% and 9.9% in the F1 score, respectively. Experimental results show that this method achieves a good balance between detection accuracy and computational efficiency, providing a reliable technical solution for the practical application of intelligent manufacturing quality monitoring systems for aluminum ingots.