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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n

文献类型： 外文期刊

作者： Zhang, Yuanyuan 1 ; Mu, Zhongqiu 1 ; Tian, Kunpeng 1 ; Zhang, Bing 2 ; Huang, Jicheng 1 ;

作者机构： 1.Minist Agr & Rural Affairs, Nanjing Inst Agr Mechanizat, Nanjing 210014, Peoples R China

2.Chinese Acad Agr Sci, Grad Sch, Beijing 100083, Peoples R China

关键词： unmanned agricultural machinery; field obstacles; YOLOv8n; lightweight; safety warning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCI

摘要： Unmanned agricultural machinery can significantly reduce labor intensity while substantially enhancing operational efficiency and production benefits. However, the presence of various obstacles in complex farmland environments is inevitable. Accurate and efficient obstacle recognition technology, along with a reliable safety warning system, is a crucial prerequisite for ensuring the safe and stable operation of unmanned agricultural machinery. This study proposes a lightweight model for farmland obstacle detection by improving the YOLOv8n object detection algorithm. Specifically, we introduce the Context-Guided Block (CG Block) in the C2f module and the Context-Guide Fusion Module (CGFM) in the Feature Pyramid Network (FPN) to enhance the model's contextual information perception during feature extraction and fusion. Additionally, we employ a Lightweight Shared Convolutional Separable Batch Normalization Detection Head in the detection head, which significantly reduces the number of parameters while improving detection accuracy. Experimental results demonstrate that our method achieves a mean average precision (mAP) of 92.3% at 59.1 frames per second (FPS). The improved model reduces parameter count and computational complexity by 31.9% and 33.4%, respectively, with a model size of only 4.2 MB. Compared to other algorithms, the proposed model maintains an optimal balance between parameter efficiency, computational cost, detection speed, and accuracy, exhibiting distinct advantages. Furthermore, we propose a safety warning strategy based on the relative velocity and distance between obstacles and the unmanned agricultural machinery. Field experiments conducted under this strategy reveal an overall warning accuracy of up to 86%, verifying the reliability of the safety warning system. This ensures that unmanned agricultural machinery can effectively mitigate potential safety risks during field operations.

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