ArticleSensors (Basel, Switzerland)2022
Detection of Farmland Obstacles Based on an Improved YOLOv5s Algorithm by Using CIoU and Anchor Box Scale Clustering.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Survey of Environmental Perception for Unmanned Ground Agricultural Machinery in Field Environments.Sensors (Basel, Switzerland) · 2026Review
- Development and clinical validation of an artificial intelligence based model for thyroid nodule malignancy risk assessment using C-TIRADS guidelines.Frontiers in medicine · 2026Article
- Enhancing multilevel tea leaf recognition based on improved YOLOv8n.Frontiers in plant science · 2025Article
- Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots.Sensors (Basel, Switzerland) · 2024Article
- A Comprehensive Survey on Visual Perception Methods for Intelligent Inspection of High Dam Hubs.Sensors (Basel, Switzerland) · 2024Review
- Enhanced batch sorting and rapid sensory analysis of Mackerel products using YOLOv5s algorithm and CBAM: Validation through TPA, colorimeter, and PLSR analysis.Food chemistry: X · 2023Article
- Obstacle Detection Method Based on RSU and Vehicle Camera Fusion.Sensors (Basel, Switzerland) · 2023Article
- A Dragon Fruit Picking Detection Method Based on YOLOv7 and PSP-Ellipse.Sensors (Basel, Switzerland) · 2023Article
- Multi-Objective Association Detection of Farmland Obstacles Based on Information Fusion of Millimeter Wave Radar and Camera.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
It is necessary to detect multi-type farmland obstacles in real time and accurately for unmanned agricultural vehicles. An improved YOLOv5s algorithm based on the K-Means clustering algorithm and CIoU Loss function was proposed to improve detection precision and speed up real-time detection. The K-Means clustering algorithm was used in order to generate anchor box scales to accelerate the convergence speed of model training. The CIoU Loss function, combining the three geometric measures of overlap area, center distance and aspect ratio, was adopted to reduce the occurrence of missed and false detection and improve detection precision. The experimental results showed that the inference time of a single image was reduced by 75% with the improved YOLOv5s algorithm; compared with that of the Faster R-CNN algorithm, real-time performance was effectively improved. Furthermore, the
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Registered trials
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