YOLO-SDL: a lightweight wheat grain detection technology based on an improved YOLOv8n model
Zhaomei Qiu, Fei Wang, Weili Wang, Tingting Li, Xin Jin, Shunhao Qing, Yi Shi · 2024
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Description
Zhaomei Qiu and co-authors present YOLO-SDL, a lightweight model to detect wheat grains. Their 2024 paper deals with grain quality, a key factor for food safety and farm income, where traditional detection is slow and inefficient.
Their model improves the YOLOv8n detector. A high-quality dataset was built with images of perfect, germinated, diseased, and damaged grains, expanded by data augmentation. ShuffleNetV2 forms the backbone, and depthwise separable convolutions join a large separable kernel attention mechanism. The design aims at fast, accurate recognition that can run on modest hardware, a step toward automated grain inspection.
Catalog record
| CREATOR | Zhaomei Qiu, Fei Wang, Weili Wang, Tingting Li, Xin Jin, Shunhao Qing, Yi Shi |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 32 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |