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01.01.2025На каком языке издана
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This paper introduces an enhanced YOLOv11 model for accurate and efficient weed detection in precision agriculture. Our improvements to YOLOv11n include integrating SPD-Conv for faster speed and better multi-scale fusion, embedding the SCSA attention mechanism in Bottlenecks to mitigate occlusion-related misses, and utilizing the CIoU loss for an improved precision-recall balance. Leveraging the CottonWeedDet12 dataset augmented with random transformations, our model achieves a 1% higher mAP than YOLOv11n and outperforms counterparts like YOLOv5/7/8. It demonstrates robust performance across varying lighting, occlusion, and complex soil conditions, offering a viable solution for smart farming applications.DOI
10.1109/RICAI68060.2025.11385045Тип публикаций
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