基于轻量级SCS-YOLO-Seg模型的花采集点的识别方法
Baojian Ma1, Zhenghao Wu2, Yun Ge2
1Department of Mechanical and Electrical Engineering, Xinjiang Institute of Technology, Aksu 843100, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究介绍了SCS-YOLO-Seg,这是一款用于自动收获花的轻量级模型. 它准确地识别了采摘点,提高了机器人花摘系统的效率.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 由于复杂的背景和鲜花姿势的变化,自动花收获在准确的采摘点识别方面面临挑战.
- 现有的方法经常在实时应用程序的效率和资源要求方面扎.
研究的目的:
- 开发一种新的,轻量级的细分模型 (SCS-YOLO-Seg),用于在自动化花采摘中准确识别采摘点.
- 增强YOLOv8n-seg架构,以提高模型压缩和细分性能.
主要方法:
- 该研究提出了SCS-YOLO-Seg,这是一个轻量级的细分模型,通过结合StarNet骨干和C2f-Star模块来增强YOLOv8n-seg.
- 一个双路径协作架构 (Seg-Marigold头) 优化了细分效率.
- 采集点通过与茎骨架相交的圆形面具配件来确定.
主要成果:
- SCS-YOLO-Seg实现了实质性的模型压缩,减少了尺寸,参数和计算复杂性.
- 该模型显示了93.36%的选择点识别精度,每个图像的平均推断时间为28.66ms.
- 与YOLOv8n-seg. 相比,它有效地平衡了与高细分精度的模型压缩.
结论:
- SCS-YOLO-Seg为视觉系统提供了一个强大而高效的解决方案,用于自动化花收获.
- 轻量级的设计使其适用于资源有限的机器人应用.
- 这种方法显著提高了自动化花收获系统的可行性.
更多相关视频
08:25Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
Published on: May 7, 2019
9.1K
11:38Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
691
相关概念视频
02:16Light Acquisition
8.6K
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.
8.6K
01:30Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device
168
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
168
01:28Methods of Classification and Identification
187
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
187
01:20Cluster Sampling Method
12.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.7K