一个轻量级的葡萄检测模型在自然环境中,基于增强的YOLOv8框架
Xinyu Wu1,2, Rong Tang1,2, Jiong Mu1,2
1College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Frontiers in plant science
|August 9, 2024
概括
本研究介绍了TiGra-YOLOv8,这是一款用于自动采摘葡萄的轻量级模型. 它显著提高了密集葡萄群的检测准确度和速度,同时减少了移动部署的计算需求.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动采摘葡萄需要精确的果实和茎检测.
- 密集的水果排列和类似的树干/树枝颜色挑战了现有的模型.
- 目前的模型往往太大,并且对于移动部署来说太慢.
研究的目的:
- 开发一种轻量级和高效的葡萄检测模型,用于自动收获.
- 为了应对密集的水果集群和类似的茎/树枝颜色的挑战.
- 为了提高检测速度和减少移动应用程序的计算负载.
主要方法:
- 提议的TiGra-YOLOv8模型基于YOLOv8n.
- 集成的注意力尺度融合 (ASF) 模块用于特征提取.
- 雇员适应性培训样本选择 (ATSS) 提高样本质量.
- 连续证据的使用权重互叠对交叉与联盟 (Wise-IoU) 损失函数的交叉.
- 通过通道修剪减少模型尺寸.
主要成果:
- 与YOLOv8n.相比,TiGra-YOLOv8在mAP中获得了3.33%的增加,比YOLOv8n.
- 检测速度提高了7.49% (FPS).
- 参数数量减少了52.19%,计算需求减少了51.72%.
- 模型大小减少了45.76%.
结论:
- TiGra-YOLOv8提高了对具有挑战性的葡萄目标的检测准确度.
- 该模型在参数和计算要求方面提供了显著的减少.
- 这种轻量级模型适合在移动设备上部署,用于自动采摘葡萄.
相关概念视频
Difference from Background: Limit of Detection
6.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
6.0K
Light Acquisition
8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Extraction: Advanced Methods
436
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
436
Force Classification
1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Aggregates Classification
310
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
310
Improving Translational Accuracy
9.6K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.6K


