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A lightweight weed detection algorithm designed based on YOLOv9
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AI
DOI：10.1016/j.asoc.2025.114183.png)
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Abstract
En 中文
• Developed a K-Means-based anchor box optimization method tailored to the input size, reducing training errors and enhancing object detection accuracy. • Introduced the SPPELAN-ECA module into the neck network, achieving improved feature fusion and model accuracy without increasing parameter count. • Proposed the AGSConv module, which reduces parameters and computational cost while improving model expressiveness and detection accuracy.
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