id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
brj-23918	Dong, Yingda; He, Chunguang; Xiang, Xiaoyang; Cui, Yuhan; Kang, Yongkang; Ding, Anning; Duo , Huaqiong; Wang, Ximing	IECAU-Net: A Wood Defects Image Segmentation Network Based on Improved Attention U-Net and Attention Mechanism	2025	12	.pdf	application/pdf	4983	229	48	Based on this, the network framework is improved, and the main research focuses on adding Convolutional Block Attention Module (CBAM), Attention Gate (AG), and Efficient Channel Attention (ECA) modules to exchange the positions of each module, optimize the loss function, and adjust the parameters of the network model to obtain the best sawn timber surface crack semantic segmentation model. In the application of deep learning algorithms for detecting wood cracks and other defects, in contrast to traditional machine learning based detection methods, the steps of wood crack segmentation and feature extraction are reduced.	cache/brj-23918.pdf	txt/brj-23918.txt