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Jun Tie, Weibo Wu, Lu Zheng, Lifeng Wu and Ting Chen
When aiming at the problems such as missed detection or misdetection of recognizing green walnuts in the natural environment directly by using target detection algorithms, a method is proposed based on improved UNet3+ for green walnut image segmentation,...
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Zhengyang Zhong, Lijun Yun, Feiyan Cheng, Zaiqing Chen and Chunjie Zhang
This paper proposes a lightweight and efficient mango detection model named Light-YOLO based on the Darknet53 structure, aiming to rapidly and accurately detect mango fruits in natural environments, effectively mitigating instances of false or missed det...
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Xiaoqin Xue, Chao Ren, Anchao Yin, Ying Zhou, Yuanyuan Liu, Cong Ding and Jiakai Lu
In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convol...
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Shubin Wang, Yuanyuan Chen and Zhang Yi
The structure and function of retinal vessels play a crucial role in diagnosing and treating various ocular and systemic diseases. Therefore, the accurate segmentation of retinal vessels is of paramount importance to assist a clinical diagnosis. U-Net ha...
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Xin Jin, Cheng Lin, Jiangtao Ji, Wenhao Li, Bo Zhang and Hongbin Suo
The extraction of navigation lines plays a crucial role in the autonomous navigation of agricultural robots. This work offers a method of ridge navigation route extraction, based on deep learning, to address the issues of poor real-time performance and l...
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Zhanlin Ji, Dashuang Yao, Rui Chen, Tao Lyu, Qinping Liao, Li Zhao and Ivan Ganchev
Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions...
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Alyaa Amer, Tryphon Lambrou and Xujiong Ye
The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder?decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an outstanding p...
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Yeonghyeon Gu, Zhegao Piao and Seong Joon Yoo
In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder?decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder?decoder model struc...
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Lin Dong, Yuanning Liu and Xiaodong Zhu
Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, d...
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Jingru Hou, Yujuan Si and Xiaoqian Yu
Application areas of image super-resolution: surveillance, medical diagnosis, Earth observation and remote sensing, astronomical observation, biometric information identification.
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