التفاصيل البيبلوغرافية
| العنوان: |
Navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network. |
| المؤلفون: |
Diao, Zhihua1 (AUTHOR), Guo, Peiliang1 (AUTHOR), Zhang, Baohua2 (AUTHOR), Zhang, Dongyan3 (AUTHOR), Yan, Jiaonan1 (AUTHOR), He, Zhendong1 (AUTHOR), Zhao, Suna1 (AUTHOR), Zhao, Chunjiang1,4 (AUTHOR) zhaocj@nercita.org.cn, Zhang, Jingcheng5 (AUTHOR) |
| المصدر: |
Computers & Electronics in Agriculture. Sep2023, Vol. 212, pN.PAG-N.PAG. 1p. |
| مصطلحات موضوعية: |
*LEAST squares, *PLANT identification, *CORN, *ROBOTS, *ALGORITHMS |
| مستخلص: |
• A navigation line extraction algorithm was proposed, which was suitable for different growth periods and complex farmland environment. • The method of locating corn plant by corn plant core was proposed. • A new spatial pyramid structure ASPPF was proposed. • An improved YOLOv8s model ASPPF-YOLOv8s was proposed for more accurate detection of corn plant cores. • The proposed navigation line extraction algorithm can meet the requirements of visual navigation of corn spraying robot. Aiming at the shortcomings of the existing navigation line extraction algorithm for corn spraying robot in complex farmland environment, such as poor extraction effect and poor adaptability, this paper proposed a navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network. This algorithm proposed to use corn plant core as the identification target to locate corn plants. Firstly, a new spatial pyramid structure - atrous spatial pyramid pooling faster (ASPPF) was proposed in this paper, and an improved YOLOv8s model ASPPF-YOLOv8s was proposed for more accurate detection of corn plant cores. Secondly, the center coordinates of the network detection box were used to locate the corn crop row feature points. Finally, the least squares method was used to fit the corn crop row lines. Experimental results showed that ASPPF-YOLOv8s network had a good effect on corn plant cores extraction under different growth periods and environmental pressures. Mean average precision (MAP) and F1 of ASPPF-YOLOv8s network increased from 86.4% and 86% of YOLOv7 network, 88.8% and 87% of YOLOv8s network, 88.6% and 89% of ASPP-YOLOv8s network to 90.2% and 91%. The average fitting time and average angle error of the centerline of crop row were reduced from 82.6 ms and 0.97° for SUSAN corner detection method combined with least square method, 74.8 ms and 0.75° for means method combined with least square method, 67 ms and 2.03° for FAST corner detection method combined with least square method to 45 ms and 0.63° by using the center coordinates of corn plant core detection box combined with least square method. The accuracy rate increased from 91.47% for SUSAN corner detection method combined with least square method, 93.6% for means method combined with least square method and 87.35% for FAST corner detection method combined with least square method to 94.35%. It shows that the navigation line extraction algorithm proposed in this paper can meet the requirements of real-time and accuracy of visual navigation of corn spraying robot, and can be used to extract navigation line of corn spraying robot in complex farmland environment. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Academic Search Index |
