基于麻鸭数据集的高效分割模型，助力智慧养鸭业落地。
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset
- 构建麻鸭数据集并设计轻量级分割模块，提升农田场景适用性。
- 分割模型达96.43% mIoU，蒸馏后学生模型仍保持94.49% mIoU。
- 成果可直接用于真实养鸭场，适合农业智能视觉应用者参考。
智慧农业现代化是提升农业生产效率与环境的重要路径。尽管诸多大模型在目标识别与分割任务中表现优异，但因可解释性差、计算量大，难以在农业领域实际应用。本文构建了包含1951张麻鸭图像的安岳麻鸭数据集，并由专业标注员完成目标检测与分割标注。基于该数据集，提出鸭群处理模块（DuckProcessing），实现真实养殖场中的麻鸭精准识别。首先，采用YOLOv8模型在测试集上达到98.10%精确率、96.53%召回率与0.95的F1分数；其次，提出的DuckSegmentation分割模型在测试集上取得96.43%的mIoU；最后，以DuckSegmentation为教师模型，通过知识蒸馏将Deeplabv3-r50作为学生模型，最终学生模型在测试集上达到94.49% mIoU。该方法为实际麻鸭智慧养殖提供了新思路。
原文摘要 · Abstract (English)
The modernization of smart farming is a way to improve agricultural production efficiency, and improve the agricultural production environment. Although many large models have achieved high accuracy in the task of object recognition and segmentation, they cannot really be put into use in the farming industry due to their own poor interpretability and limitations in computational volume. In this paper, we built AnYue Shelduck Dateset, which contains a total of 1951 Shelduck datasets, and performed target detection and segmentation annotation with the help of professional annotators. Based on AnYue ShelduckDateset, this paper describes DuckProcessing, an efficient and powerful module for duck identification based on real shelduckfarms. First of all, using the YOLOv8 module designed to divide the mahjong between them, Precision reached 98.10%, Recall reached 96.53% and F1 score reached 0.95 on the test set. Again using the DuckSegmentation segmentation model, DuckSegmentation reached 96.43% mIoU. Finally, the excellent DuckSegmentation was used as the teacher model, and through knowledge distillation, Deeplabv3 r50 was used as the student model, and the final student model achieved 94.49% mIoU on the test set. The method provides a new way of thinking in practical sisal duck smart farming.
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