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| Seed Object Detection Method based on Improved YOLOv8 |
| WANG Ying*, LIANG Qiuyang*, CHANG Guangliang**, LIU Qi*
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1.School of Information and Control Engineering，Jilin Institute of Chemical Technology，Jilin City 132022，China；2. Kesshida (Changchun) Automobile Electrical Appliance Co., LTD., Changchun, Jilin 130000, China
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Abstract Aiming at the problems of low manual sorting efficiency and slow detection speed and low recognition accuracy of existing machine vision recognition systems in the current process crop seed sorting， a seed target detection method based on improved YOLOv8n is proposed. First，in C2f，the lightweight network FasterNet is used replace Bottleneck， which reduces redundant calculations and memory access， and improves the network operation speed and capability. Second， the EMA attention mechanism is introduced， which uses parallel sub structure and cross-space information aggregation to focus better on multi-scale features，and improve the accuracy of seed recognition. Finally， the Wise-IOU-v3 loss function used，which reduces the impact of low-quality annotations and accelerates the convergence speed of the network. The experimental results show that compared with the standard YOLOv8 algorithm， the improved YOLOv8n algorithm has improved by 2.2%， 3.4%， 1.4%， and 4.4 in precision，recall，mAP(0.5)，and mAP(0.5:0.95)，and the FLOPs are reduced by 20.2%，and the number of parameters is reduced by 27.2%. The improved YOLOv8n shows significant advantages in the trade-off precision and speed， and it can provide more reliable technical support for agricultural automatic sorting equipment.
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Published: 21 December 2025
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