Zobrazeno 1 - 10
of 62
pro vyhledávání: '"improved YOLOv7"'
Autor:
Yuxin Xia, Wenxia Yuan, Shihao Zhang, Qiaomei Wang, Xiaohui Liu, Houqiao Wang, Yamin Wu, Chunhua Yang, Jiayi Xu, Lei Li, Junjie He, Zhiyong Cao, Zejun Wang, Zihua Zhao, Baijuan Wang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to r
Externí odkaz:
https://doaj.org/article/967a195f14fc4a54a83cbbc8dd11f0d5
Publikováno v:
IEEE Access, Vol 12, Pp 24453-24464 (2024)
The complex environmental influences often complicate the detection of longitudinal tears in conveyor belts, resulting in insufficient detection accuracy, overlooked detection, and elevated false detection rates. In this study, we propose a new depth
Externí odkaz:
https://doaj.org/article/f42471ce938f49eaa457fac36d1d4240
Autor:
Junjie He, Shihao Zhang, Chunhua Yang, Houqiao Wang, Jun Gao, Wei Huang, Qiaomei Wang, Xinghua Wang, Wenxia Yuan, Yamin Wu, Lei Li, Jiayi Xu, Zejun Wang, Rukui Zhang, Baijuan Wang
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
IntroductionIn order to solve the problem of precise identification and counting of tea pests, this study has proposed a novel tea pest identification method based on improved YOLOv7 network.MethodsThis method used MPDIoU to optimize the original los
Externí odkaz:
https://doaj.org/article/f601dd18b38146008070192fe08c5a38
Publikováno v:
Remote Sensing, Vol 16, Iss 6, p 1002 (2024)
Due to the broad usage and widespread popularity of drones, the demand for a more accurate object detection algorithm for images captured by drone platforms has become increasingly urgent. This article addresses this issue by first analyzing the uniq
Externí odkaz:
https://doaj.org/article/61ce244ce09642e397171ad74f8d6469
Publikováno v:
IEEE Access, Vol 11, Pp 133086-133098 (2023)
Traditional agricultural practices of hand-picking ripe tomatoes are labor-intensive and inefficient for large-scale harvesting. To address this, we propose an innovative approach using the YOLOv7 algorithm for ripe tomato detection, enabling robotic
Externí odkaz:
https://doaj.org/article/6f4bd5d1bc1a4ba3bba5fd41bf0833a3
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
IntroductionReal-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process.MethodsTo reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occlud
Externí odkaz:
https://doaj.org/article/2f6bd8950d2d4e3a8792a79a049ef74a
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Forests, Vol 14, Iss 7, p 1453 (2023)
Trunk recognition is a critical technology for Camellia oleifera fruit harvesting robots, as it enables accurate and efficient detection and localization of vibration or picking points in unstructured natural environments. Traditional trunk detection
Externí odkaz:
https://doaj.org/article/a05af148e6ad493c821d5c75bd7029b5