一个轻量级的方法,全面的织物异常检测建模
Shuqin Cui1, Weihong Liu1, Min Li1
1School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
我们开发了GH-YOLOx,这是一种用于检测织物异常的轻量级网络. 这种高效的模型降低了计算成本并提高了检测率,使其成为移动设备实时应用的理想选择.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 织物异常检测对于质量控制至关重要.
- 现有的方法往往需要高计算资源,限制实时应用.
- 需要轻量级和高效的模型来部署在边缘设备上.
研究的目的:
- 提出一个轻量级的网络,GH-YOLOx,用于高效的织物异常检测.
- 为了减少计算资源的消耗,同时保持高的检测准确度.
- 在移动和嵌入式设备上实现实时织物异常检测.
主要方法:
- 集成幽灵卷曲和层次的GHNetV2骨干来捕获多尺度的特征.
- 实施GhostConv,动态卷积,功能融合模块和共享组卷积头.
- 应用灯光修剪用于推断加速和通道智能知识蒸以提高准确性.
主要成果:
- 与现有的轻量级模型相比,GH-YOLOx显著降低了参数数量.
- 拟议的网络在织物异常检测任务中实现了更高的检测率.
- 实验结果验证了GH-YOLOx模型的有效性和效率.
结论:
- GH-YOLOx为实时织物异常检测提供了实用和高效的解决方案.
- 轻量化设计使其适合在资源有限的移动和嵌入式系统上部署.
- 这种方法解决了织物检查中高计算成本的挑战.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
19
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
19
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
30
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
30


