MFSnet:一个多级特征选网络,用于在密集环境中计数.
1College of Mechanical Engineering, University of South China, Hengyang, China.
British poultry science
|June 2, 2025
概括
这项研究介绍了MFSnet,这是一个新的深度学习网络,用于在密集的环境中准确地计算. 该方法显著提高了计数精度,提高了家禽养殖场的效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 农业技术 农业技术
背景情况:
- 机器视觉提供了高效的计数,但高密度场景会导致重叠的,阻碍准确性.
- 准确的种群计数对于优化自由放牧家禽养殖业务至关重要.
研究的目的:
- 开发和验证一个专门的计数网络 (MFSnet),用于在人口密的放牧环境中准确计数.
- 提高农业环境中自动化种群监测的效率和准确性.
主要方法:
- 拟议的MFSnet使用多尺度特征提取和特征选模块 (FSM) 来增强的识别.
- 为了培训和验证,创建了一个新的数据集,Chicken2023,包括550张图像和49,747只标记的.
- 网络的性能与现有的计数算法进行了评估,使用了诸如平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 等指标.
主要成果:
- 在Chicken2023数据集上,MFSnet实现了卓越的计数性能,MAE为2.7和RMSE为3.6.
- 与现有的表现最好的网络相比,MFSnet显示MAE减少了6.25%,RMSE减少了6.26%.
- 拟议的网络准确地识别了的数量,即使在具有挑战性的密集环境中也是如此.
结论:
- 在密集的环境中,MFSnet有效地解决了在密集环境中计数重叠的的挑战.
- 开发的网络模型通过精确的自动化人口监测来提高家禽养殖的效率.
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