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題名:釋迦表面瑕疵偵測系統:深度學習的應用
書刊名:數據分析
作者:黃渟晏簡于婷康智翔陳思翰
作者(外文):Huang, Ting-yanJian, Yu TingKang, Chih-hsiangChen, Ssu-han
出版日期:2021
卷期:16:2
頁次:頁21-41
主題關鍵詞:鳳梨釋迦瑕疵偵測實驗設計Custard appleDefect detectionYOLOv4Design of experiment
原始連結:連回原系統網址new window
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  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:7
期刊論文
1.Chen, S. H.、Kang, C. H.、Perng, D. B.(2020)。Detecting and measuring defects in wafer die using GAN and YOLOv3。Applied Sciences,10(23),(8725)1-(8725)15。  new window
2.Fu, L.、Duan, J.、Zou, X.、Lin, J.、Zhao, L.、Li, J.、Yang, Z.(2020)。Fast and accurate detection of banana fruits in complex background orchards。IEEE Access,8,196835-196846。  new window
3.Koirala, A.、Walsh, K. B.、Wang, Z.、McCarthy, C.(2019)。Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of 'MangoYOLO'。Precision Agriculture,20(6),1107-1135。  new window
4.Liu, G.、Nouaze, J. C.、Touko Mbouembe, P. L.、Kim, J. H.(2020)。YOLO-tomato: A robust algorithm for tomato detection based on YOLOv3。Sensors,20(7),(2145)1-(2145)20。  new window
5.Liu, L.、Ouyang, W.、Wang, X.、Fieguth, P.、Chen, J.、Liu, X.、Pietikäinen, M.(2020)。Deep learning for generic object detection: A survey。International Journal of Computer Vision,128(2),261-318。  new window
6.Luo, Z.、Yu, H.、Zhang, Y.(2020)。Pine cone detection using boundary equilibrium generative adversarial networks and improved YOLOv3 model。Sensors,20(16),(4430)1-(4430)18。  new window
7.Stein, M.、Bargoti, S.、Underwood, J.(2016)。Image based mango fruit detection, localisation and yield estimation using multiple view geometry。Sensors,16(11),(1915)1-(1915)25。  new window
8.Sa, I.、Ge, Z.、Dayoub, F.、Upcroft, B.、Perez, T.、McCool, C.(2016)。Deepfruits: A fruit detection system using deep neural networks。Sensors,16(8),(1222)1-(1222)23。  new window
9.Tian, Y.、Yang, G.、Wang, Z.、Wang, H.、Li, E.、Liang, Z.(2019)。Apple detection during different growth stages in orchards using the improved YOLO-V3 model。Computers and Electronics in Agriculture,157,417-426。  new window
10.Tian, Y.、Yang, G.、Wang, Z.、Li, E.、Liang, Z.(2019)。Detection of apple lesions in orchards based on deep learning methods of cyclegan and yolov3-dense。Journal of Sensors,2019。  new window
會議論文
1.江淑雯、黃政龍(2017)。鳳梨釋迦外銷集貨包裝場作業模式及設備研發115-127。  延伸查詢new window
2.Redmon, J.、Farhadi, A.(2017)。YOLO9000: better, faster, stronger。The IEEE Conference on Computer Vision and Pattern Recognition,7263-7271。  new window
3.Redmon, Joseph、Divvala, Santosh、Girshick, Ross、Farhadi, Ali(2016)。You only look once: Unified, real-time object detection。The 29th IEEE Conference on Computer Vision and Pattern Recognition,(會議日期: 27-30 June 2016),779-788。  new window
單篇論文
1.Redmon, Joseph,Farhadi, Ali(2018)。YOLOv3: An incremental improvement,https://doi.org/10.48550/arXiv.1804.02767,(1804.02767)。  new window
2.Garcia-Garcia, A.,Orts-Escolano, S.,Oprea, S.,Villena-Martinez, V.,Garcia-Rodriguez, J.(2017)。A review on deep learning techniques applied to semantic segmentation(1704.06857)。  new window
3.Gong, B.,Ergu, D.,Cai, Y.,Ma, B.(2020)。A method for wheat head detection based on Yolov4。  new window
4.Lim, J.,Ahn, H. S.,Nejati, M.,Bell, J.,Williams, H.,MacDonald, B. A.(2020)。Deep neural network based real-time kiwi fruit flower detection in an orchard environment(2006.04343)。  new window
5.Bochkovskiy, Alexey,Wang, Chien-Yao,Liao, Hong-Yuan Mark(2020)。YOLOv4: Optimal Speed and Accuracy of Object Detection,https://doi.org/10.48550/arXiv.2004.10934,(2004.10934v1)。  new window
 
 
 
 
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