id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-29392	Liao, Wenbing; Li, Wenwen	Research on DETR-based Weed Detection Algorithm	2025	5	.pdf	application/pdf	3442	165	50	Model detection results Fig. Wenbing Liao 1 and Wenwen Li 2 1 School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin Jilin 132022, China 2 School of Mechanical and Control Engineering, Baicheng Normal College, Baicheng, Jilin 137000, China Abstract: To address the problem of complex field conditions and high similarity between corn seedlings and weeds, this study proposes an improved DETR (Detection Transformer) model for weed detection in corn fields, which uses the CBAM convolutional attention mechanism in the DETR model, and uses a focal loss function instead of the traditional cross-entropy loss function to balance the number of positive and negative class samples of the data.	cache/fcis-29392.pdf	txt/fcis-29392.txt