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Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense 期刊论文
Journal of Sensors, 2019, 期号: 2019, 页码: 1-13
作者:  Tian YN(田雨农)
Adobe PDF(26755Kb)  |  收藏  |  浏览/下载:137/34  |  提交时间:2022/01/06
optical sensors  deep learning  lesion detection  CycleGAN  DenseNet  YOLO-V3 model  
High-quality 3D Reconstruction with Depth Super-resolution and Completion 期刊论文
IEEE Access, 2019, 卷号: 7, 期号: 1, 页码: 19370-19381
作者:  Li JW(李建伟);  Gao W(高伟);  Wu YH(吴毅红)
浏览  |  Adobe PDF(4152Kb)  |  收藏  |  浏览/下载:320/94  |  提交时间:2019/04/22
Deep Learning  Super-resolution  Image Processing  Depth Completion  3d Reconstruction  
Apple detection during different growth stages in orchards using the improved YOLOV3 model 期刊论文
Computers and Electronics in Agriculture, 2019, 期号: 157, 页码: 417-426
作者:  Tian YN(田雨农)
Adobe PDF(4028Kb)  |  收藏  |  浏览/下载:128/40  |  提交时间:2022/01/07
Apple images acquisition  Image augmentation  Deep learning  YOLOV3-dense  Real-time detection  
BRANT: A Versatile and Extendable Resting-State fMRI Toolkit 期刊论文
FRONTIERS IN NEUROINFORMATICS, 2018, 卷号: 12, 期号: -, 页码: 52
作者:  Xu, Kaibin;  Liu, Yong;  Zhan, Yafeng;  Ren, Jiaji;  Jiang, Tianzi
浏览  |  Adobe PDF(4680Kb)  |  收藏  |  浏览/下载:316/39  |  提交时间:2018/10/10
BRANT  resting-state fMRI  code-generated GUI  preprocessing  visualization