在多场景中使用改进的DeepLabV3+方法进行牛目标细分方法
Tao Feng1, Yangyang Guo1,2, Xiaoping Huang1,2
1School of Internet, Anhui University, Hefei 230039, China.
Animals : an open access journal from MDPI
|August 12, 2023
概括
这项研究引入了一个改进的DeepLabV3+模型,用于在复杂的农业环境中精确地对动物进行细分,通过更好的特征提取和融合来增强智能动物养殖.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 动物科学动物科学
背景情况:
- 准确的动物检测和定位对于了解动物行为和推进智能动物养殖至关重要.
- 复杂的繁殖环境对现有的语义细分模型构成重大挑战,导致目标细分差,概括性弱.
研究的目的:
- 为复杂的畜牧场景开发一个更有效的语义细分模型.
- 为了提高目标细分精度和模型概括能力.
主要方法:
- 提出了一个改进的DeepLabV3+网络 (Imp-DeepLabV3+),用MobileNetV2取代骨干,以增强功能提取.
- 在解码器阶段实施了层层的功能融合方法,用于语义和高分辨率特征的多尺度集成.
- 压缩和刺激 (SENet) 模块被纳入,以提高特征融合和细分精度.
主要成果:
- Imp-DeepLabV3+模型实现了高性能指标:99.4%的像素精度 (PA),98.1%的平均像素精度 (MPA) 和96.8%的平均交叉点与联盟 (MIoU).
- 与原来的DeepLabV3+相比,改进后的模型显示了显著增强的细分性能.
- Imp-DeepLabV3+的表现优于其他常见的语义细分模型,如FCNs,LR-ASPP和U-Net.
结论:
- 拟议的Imp-DeepLabV3+模型在具有挑战性的环境中为动物细分提供了卓越的性能.
- 这种进步非常适用于场景细分任务,个人信息分析和智能动物养殖系统的开发.
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