Navigation line detection algorithm for corn spraying robot based on improved LT-YOLOv10s
文献类型: 外文期刊
第一作者: Diao, Zhihua
作者: Diao, Zhihua;Ma, Shushuai;Li, Xingyi;Zhao, Suna;He, Yan;Li, Jiangbo;Zhang, Jingcheng;Zhang, Baohua;Jiang, Liying;Jiang, Liying
作者机构:
关键词: Deep learning; Corn spraying robot; Navigation line detection; Lightweight network
期刊名称:PRECISION AGRICULTURE ( 影响因子:6.6; 五年影响因子:7.4 )
ISSN: 1385-2256
年卷期: 2025 年 26 卷 3 期
页码:
收录情况: SCI
摘要: The deep integration of artificial intelligence technology and agriculture has significantly propelled the rapid development of smart agriculture. However, the field still faces numerous challenges, including high algorithm complexity and limited detection speed in farmland environments. To address the challenges encountered by corn spraying robots in navigating and identifying lines, we have proposed a corn crop row navigation line recognition algorithm based on the LT-YOLOv10s model. By introducing lightweight network models (GhosNet), efficient feature pyramid models (SPPFA), and efficient feature attention modules (PSCA) into the YOLOv10s network, we have reduced the complexity of the model and significantly enhanced the detection efficiency of corn plants. Then, the algorithm precisely locates corn plants using the center points of detection boxes and accurately fits crop rows using the least squares method. Finally, the navigation lines centered on the corn crop rows are determined through the adjacent centerline method. Experimental data significantly demonstrates that the comprehensive performance of the LT-YOLOv10s model surpasses industry benchmark models such as YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and the traditional YOLOv10s. The proposed algorithm for extracting the center navigation line of corn crop rows boasts an average fitting time of just 26ms with an accuracy rate of up to 93.8%, ensuring precision and reliability in navigation line extraction. This provides robust technical support for precise navigation of corn-spraying robots.
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