基于改进的MobileNetV3的物种分类和实施
1College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China.
Journal of food science
|April 4, 2025
概括
这项研究引入了一个改进的AI模型用于分类,实现了高精度和稳定性. 开发的系统可在各种平台上部署,有助于在研究和工业中识别.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 植物学 植物学
背景情况:
- 目前的分类方法在一般化和部署方面扎.
- 需要强大而高效的识别系统.
研究的目的:
- 系统地比较五种类型的物种分类模型.
- 开发和部署一个优化的MobileNetV3模型,并采用新的培训策略.
主要方法:
- 变压器和卷积神经网络 (CNN) 的比较分析.
- 使用自适应混合优化器 (AHO) 和动态循环学习速率实现转移学习.
- 在一个定制的3633图像数据集上进行培训,验证和部署.
主要成果:
- 实现了98.13%的验证准确率和97.98%的平均测试准确率.
- 证明模型稳定性与最小的验证损失波动 (0.0343).
- 跨类的平衡回忆和F1分数,表明针对类间相似性的强大表现.
结论:
- 优化的MobileNetV3模型为分类提供了高精度和稳定性.
- 该模型在PC,Android和嵌入式平台上的成功部署证实了其实际适用性.
- 为研究,野生采摘和自动分类提供了宝贵的工具,支持产业.
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