DFCCNet:基于密度图回归的密集群计数网络
Jinze Lv1, Jinfeng Wang1,2, Chaoda Peng1
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.
Animals : an open access journal from MDPI
|December 9, 2023
概括
一种新的人工智能方法,密集群计数网络 (DFCCNet),准确地计算密集群中的. 这种方法提高了稳定性和精度,解决了诸如照明不良和家禽养殖中闭塞等挑战.
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:
- 自动计数对于现代家禽管理至关重要.
- 现有的方法面临着诸如照明不良,尺寸不规则,群体密集等挑战,导致计数不准确和不稳定.
- 在密集的农业环境中,需要强大的自动计数解决方案.
研究的目的:
- 提出一种新的深度学习网络,即密集的群计数网络 (DFCCNet),用于准确和稳定的自动化计数.
- 解决处理密集群体和具有挑战性的环境条件的现有方法的局限性.
- 为密集群计数研究提供基准数据集.
主要方法:
- 基于密度图回归开发了DFCCNet,结合了不同层次的特征融合,以增强背景区分.
- 实施多尺度技术来检测和计数各种大小的,提高准确性和性能.
- 使用特征卷积内核来提取精确的目标信息,减轻阻塞效应以实现可靠的计数.
主要成果:
- 在三个密度级别中,DFCCNet实现了强大的性能,平均绝对误差为4.26,9.85和19.17.
- 该方法显示了每秒16.15 (FPS) 的处理速度.
- 一个新的基准数据集,Dense-Chicken,包括600张图像和99916只标记的,被创建并提供.
结论:
- 在密集的农业环境中,DFCCNet提供了一个自动,快速和准确的解决方案来计算.
- 该网络能够处理具有挑战性的条件及其高处理速度使其适合于现实世界的应用.
- DFCCNet可以集成到手持设备中,促进在农业工程和家禽管理中的实际应用.
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