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使用AD-YOLO和MR-SORT进行自动果检测和计数

Xueliang Yang¹, Yapeng Gao¹, Mengyu Yin¹

¹College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China.

Sensors (Basel, Switzerland)

|November 9, 2024

概括

用新的视频追踪方法MR-SORT改进了果园中的果实计数的准确性. 这种方法增强了果检测,并减少了跟踪错误,以更好地估计产量.

科学领域:

农业技术农业技术
计算机视觉计算机视觉计算机视觉
机器学习机器学习

背景情况:

准确的果实计数对于农业生产管理至关重要,影响产量估计和决策.
现有的追踪通过检测算法在果园中扎着阻塞和照明变化,阻碍了自动和精确的果计数.

研究的目的:

开发一种先进的基于视频的多个对象跟踪方法,用于在复杂的农业环境中准确计数水果.
提高对象检测和跟踪算法的性能,以改善果园产量估计.

主要方法:

拟议的AD-YOLO模型整合了全维动态卷积 (ODConv),全球注意力机制 (GAM) 和软空间金字塔聚合层 (SSPPL) 以改进检测.
通过结合验证机制,SURF特征描述符和本地聚合描述符 (VLAD) 的矢量来进行强大的跟踪,开发了一个增强的BoT-SORT算法.
利用基于视频的多个对象跟踪来实时计算果实在果园设置中.

主要成果:

与标准YOLOv8.8相比,AD-YOLO模型实现了3.1%更高的mAP (96.4%).
改进的跟踪算法使ID开关减少了35.6% (减少了297个).
实现了85.6%的多对象跟踪精度,平均计数误差为0.07,R2为0.98.

结论:

该MR-SORT方法显著提高了果园自动果子计数的准确性和可靠性.
增强的检测和跟踪能力为农业产量估计和管理提供了强大的解决方案.
提出的方法表明,除了果之外,还有可能准确计算各种水果类型.

关键词:

这就是BoT-SORT.这就是YOLOv8的意义.外观特征形象特征形象特征果的数量计算的果.多个对象跟踪跟踪.

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