通过深度学习和超声波成像进行猪腹的自动质量评估
Tianshuo Wang1, Huan Yang1, Chunlei Zhang1
1College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
Animals : an open access journal from MDPI
|August 10, 2024
概括
这项研究开发了一种深度学习模型,从B型超声波图像中准确计算猪腹层,以帮助牲畜育种决策. ResNet18模型实现了高精度,使猪腹质量的实时评估成为可能.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 猪肚子质量,由肌肉和脂肪层决定,至关重要,但在专注于瘦肉的育种计划中往往是次要的.
- 准确评估猪腹层对于畜牧养殖和质量控制至关重要.
研究的目的:
- 开发和验证一个深度学习模型,使用B超声波图像准确评估猪腹层的数量.
- 从不足的语义细分转向更有效的图像分类方法来计算层数.
主要方法:
- 猪腹B型超声波图像的数据集根据层数 (3,5或7层) 被分为三组.
- 调整并训练ResNet18模型架构用于图像分类,以预测猪腹层特征.
- 模型性能使用准确性,损失和可解释性分析方法 (如 grad-CAM) 进行评估.
主要成果:
- 在分类猪腹层方面,ResNet18模型取得了很高的准确性 (99.99%的培训,96.22%的验证).
- 该模型通过可解释的分析证明了它的稳定性和可靠性.
- 实时部署处理的B超声波视频,识别层数>70%的信心.
结论:
- 深度学习,特别是ResNet18模型,提供了一种高效和精确的方法,用于在体内评估猪腹层.
- 开发的系统支持繁殖计划的立即决策,改善猪肉质量评估.
- 这项技术在畜牧养殖和猪肉质量评估方面取得了重大进展.
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