Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n
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, al...
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MDPI AG
2025-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1158 |
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| author | Yuanyuan Zhang Zhongqiu Mu Kunpeng Tian Bing Zhang Jicheng Huang |
| author_facet | Yuanyuan Zhang Zhongqiu Mu Kunpeng Tian Bing Zhang Jicheng Huang |
| author_sort | Yuanyuan Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7efee2be58804002883779e8c8848b9d |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-7efee2be58804002883779e8c8848b9d2025-08-20T01:57:04ZengMDPI AGAgronomy2073-43952025-05-01155115810.3390/agronomy15051158Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8nYuanyuan Zhang0Zhongqiu Mu1Kunpeng Tian2Bing Zhang3Jicheng Huang4Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, ChinaNanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, ChinaNanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, ChinaGraduate School of Chinese Academy of Agricultural Sciences, Beijing 100083, ChinaNanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, ChinaUnmanned 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.https://www.mdpi.com/2073-4395/15/5/1158unmanned agricultural machineryfield obstaclesYOLOv8nlightweightsafety warning |
| spellingShingle | Yuanyuan Zhang Zhongqiu Mu Kunpeng Tian Bing Zhang Jicheng Huang Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n Agronomy unmanned agricultural machinery field obstacles YOLOv8n lightweight safety warning |
| title | Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n |
| title_full | Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n |
| title_fullStr | Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n |
| title_full_unstemmed | Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n |
| title_short | Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n |
| title_sort | design and research on a reed field obstacle detection and safety warning system based on improved yolov8n |
| topic | unmanned agricultural machinery field obstacles YOLOv8n lightweight safety warning |
| url | https://www.mdpi.com/2073-4395/15/5/1158 |
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