Improved wheat ear recognition method based on GAM-YOLOv8n

Shiqiang Song; Zuoshi Liu; Yaohui Xu

doi:10.1117/12.3049594

8 November 2024 Improved wheat ear recognition method based on GAM-YOLOv8n

Shiqiang Song, Zuoshi Liu, Yaohui Xu

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163B (2024) https://doi.org/10.1117/12.3049594
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

In view of the irregular shape of wheat ears, susceptibility to environmental light, and the dense and occluded state in the field, traditional methods of wheat ear recognition suffer from low accuracy, high error rates, and inefficiency. This paper proposes a GAM-YOLOv8n deep learning model for wheat ear detection. First, the GAM (Global Attention Module) is proposed to improve the capability of capturing features of wheat ears in the farmland. Next, the Wise IoU loss function is used to replace the original loss function of CIoU for a more precise measurement of the similarity between the real and predicted bounding boxes of the wheat ears. The improved show that compared with the initial YOLOv8n network, the improved network performance increases the Precison (P) by 3.7%, detection precision (PR) by 1.1%, and mean Average Precision (mAP) by 2.7%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shiqiang Song, Zuoshi Liu, and Yaohui Xu "Improved wheat ear recognition method based on GAM-YOLOv8n", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163B (8 November 2024); https://doi.org/10.1117/12.3049594

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