Zobrazeno 1 - 10
of 58
pro vyhledávání: '"MPDIou"'
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
IntroductionRoad cracks significantly shorten the service life of roads. Manual detection methods are inefficient and costly. The YOLOv5 model has made some progress in road crack detection. However, issues arise when deployed on edge computing devic
Externí odkaz:
https://doaj.org/article/d4ae9a8023fd42119dbfb412a095d16f
Autor:
Jinyu Ou, Yijun Shen
Publikováno v:
IEEE Access, Vol 12, Pp 105165-105177 (2024)
Underwater target detection has developed greatly in recent years. However, the accuracy of underwater target detection is limited by the complex underwater environment. Based on YOLOv7, we propose an underwater object detection algorithm model to im
Externí odkaz:
https://doaj.org/article/845a14e3eb27418c9b1ee44d8487557b
Publikováno v:
IEEE Access, Vol 12, Pp 104126-104137 (2024)
Visual navigation is the pivotal technology for enabling autonomous operations of orchard robots. To obtain orchard navigation lines, the robot needs to quickly identify the positions of tree trunks. For this, we proposed a detection model called YOL
Externí odkaz:
https://doaj.org/article/304619c5aab2495ba26149f0e0eb317a
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Tea bud detection is the first step in the precise picking of famous teas. Accurate and fast tea bud detection is crucial for achieving intelligent tea bud picking. However, existing detection methods still exhibit limitations in both detection accur
Externí odkaz:
https://doaj.org/article/85ca2ab85dcc40d79ad3611021cd116f
Authenticity identification method for calligraphy regular script based on improved YOLOv7 algorithm
Autor:
Jinyuan Chen, Zucheng Huang, Xuyao Jiang, Hai Yuan, Weijun Wang, Jian Wang, Xintong Wang, Zheng Xu
Publikováno v:
Frontiers in Physics, Vol 12 (2024)
A regular calligraphy script of each calligrapher has unique strokes, and a script’s authenticity can be identified by comparing them. Hence, this study introduces a method for identifying the authenticity of regular script calligraphy works based
Externí odkaz:
https://doaj.org/article/66e1c4ac3eff4433a6ad6c1872b62db7
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 7591 (2024)
This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might
Externí odkaz:
https://doaj.org/article/be79eea987eb440d87813307b1508cf6
Publikováno v:
Sensors, Vol 24, Iss 15, p 4786 (2024)
Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection an
Externí odkaz:
https://doaj.org/article/bba3cd7290894b6ea1bb2ed4805a87b6
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:
World Electric Vehicle Journal, Vol 15, Iss 7, p 285 (2024)
Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign
Externí odkaz:
https://doaj.org/article/a32a8d1bea524bd5a4bf88ba16ae9869
Autor:
Yuxin Xia, Zejun Wang, Zhiyong Cao, Yaping Chen, Limei Li, Lijiao Chen, Shihao Zhang, Chun Wang, Hongxu Li, Baijuan Wang
Publikováno v:
Agronomy, Vol 14, Iss 6, p 1251 (2024)
Grading tea leaves efficiently in a natural environment is a crucial technological foundation for the automation of tea-picking robots. In this study, to solve the problems of dense distribution, limited feature-extraction ability, and false detectio
Externí odkaz:
https://doaj.org/article/2ca7e27d8b434d56b0de46783a810f25