@Article{cmc.2020.012328,
AUTHOR = {Yuanyuan Liu, Shuo Zhang, Haiye Yu, Yueyong Wang, Yuehan Feng, Jiahui Sun, Xiaokang Zhou},
TITLE = {Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {247--262},
URL = {http://www.techscience.com/cmc/v66n1/40444},
ISSN = {1546-2226},
ABSTRACT = {Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition
has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded
equipment deployment. Based on these problems, we proposed a novel deep
learning model with high accuracy, small model size and fast running speed
named Residual Unet with Attention mechanism using depthwise convolution
(RADw–UNet). This algorithm is based on the UNet symmetric codec model.
All the feature extraction modules of the network adopt the residual structure,
and the whole network only adopts 8 times the downsampling rate to reduce
the redundant parameters. To better extract the semantic information of the spatial
and channel dimensions, the depthwise convolutional residual block is designed
to be used in feature maps with larger depths to reduce the number of parameters
while improving the model accuracy. Meanwhile, the multi–level attention
mechanism is introduced in the skip connection to effectively integrate the information of the low–level and high–level feature maps. The experimental results
showed that the segmentation performance of RADw–UNet outperformed traditional methods and the UNet algorithm. The algorithm achieved an mIoU of
94.9%, the number of trainable parameters was only approximately 0.26 M,
and the running time for a single picture was less than 0.03 s.},
DOI = {10.32604/cmc.2020.012328}
}