New YOLO Model Enhances Cotton Square Detection with Structural and Frequency Cues

Researchers have developed a new object detection framework called Cotton-SF YOLO, designed to accurately identify cotton squares in complex field environments. This model incorporates Dynamic Snake Convolution to better capture the small, irregular boundaries of cotton squares and a frequency-domain feature modulation module to enhance discriminative edge and texture cues. Tested on a newly created dataset, Cotton-SF YOLO demonstrated improved performance over the baseline YOLO26m, achieving higher mAP and recall values. AI

IMPACT This research could improve precision agriculture by enabling more accurate automated monitoring of cotton growth.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Cotton-SF YOLO
• YOLO26m

• paper
• model release

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