id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-32158	Liu, Guangxu; Nasheng, Batu; Zheng, Wei ; Lv, Liangliang; Zhang, Tianhua ; Sun, Guodong	Small Target Defect Detection Method on Aluminum Ingot Surface based on Improved YOLOv8n-SimAM	2025	5	.pdf	application/pdf	3360	166	41	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. SimAM model achieved a mAP of 64.5% in aluminum ingot surface defect detection, which was 9 percentage points higher than the baseline model.	cache/fcis-32158.pdf	txt/fcis-32158.txt