id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-24867	Qin, Haiyang; Tan, Gongquan; Deng, Hao; Cai, Dayang; Wang, Yao; Mao, Guobin; Hu, Teng	An Infrared Ship Detection Algorithm based on YOLOv8n	2024	6	.pdf	application/pdf	4217	207	53	In summary, although some progress has been made in the current field of infrared ship detection, there are still problems such as missed detection, false detection, and large model size. From the figures, it can be seen that the improved algorithm has varying degrees of detection accuracy on seven types of ships, includifng liners mAP@0.5 Improved from 0.889 to 0.923, overall, the improved YOLO model has an average precision mean across all categories( mAP@0.5 )The improvement from 0.889 to 0.923 indicates that the improved model performs better in infrared ship detection and can more accurately detect and identify different types of ships Figure 7. (a) PR curve before improvement Figure Figure 7. (b) PR curve after improvement 4.3.	cache/ajst-24867.pdf	txt/ajst-24867.txt