标题： 基于合成数据训练的卷积神经网络的即时粒径分布测量 显示英文标题

标题： Instant Particle Size Distribution Measurement Using CNNs Trained on Synthetic Data

摘要： 准确的粒径分布（PSD）测量在矿业、制药和肥料制造等行业中非常重要，显著影响产品质量和操作效率。 传统的PSD方法如筛分分析和激光衍射是手动的、耗时的，并且受到粒子重叠的限制。 卷积神经网络（CNN）的最新发展使得可以直接从粒子图像中实现自动化、实时的PSD估计。 在本工作中，我们提出了一种基于CNN的方法，该方法使用Blender的先进渲染功能生成的真实感合成粒子图像进行训练。 使用此方法的合成数据集可以通过系统地改变粒子形状、纹理、光照和空间排列来复制各种工业场景，这些场景非常接近实际配置。 我们评估了三种基于CNN的架构，ResNet-50，InceptionV3和EfficientNet-B0，用于预测关键的PSD参数（d10，d50，d90）。 结果表明各模型的准确性相当，其中EfficientNet-B0在计算效率方面表现最佳，适合实时工业部署。 这种方法展示了真实感合成数据在稳健CNN训练中的有效性，这为自动化工业PSD监测提供了巨大的潜力。 代码已发布在：https://github.com/YasserElj/Synthetic-Granular-Gen 显示英文摘要

摘要： Accurate particle size distribution (PSD) measurement is important in industries such as mining, pharmaceuticals, and fertilizer manufacturing, significantly influencing product quality and operational efficiency. Traditional PSD methods like sieve analysis and laser diffraction are manual, time-consuming, and limited by particle overlap. Recent developments in convolutional neural networks (CNNs) enable automated, real-time PSD estimation directly from particle images. In this work, we present a CNN-based methodology trained on realistic synthetic particle imagery generated using Blender's advanced rendering capabilities. Synthetic data sets using this method can replicate various industrial scenarios by systematically varying particle shapes, textures, lighting, and spatial arrangements that closely resemble the actual configurations. We evaluated three CNN-based architectures, ResNet-50, InceptionV3, and EfficientNet-B0, for predicting critical PSD parameters (d10, d50, d90). Results demonstrated comparable accuracy across models, with EfficientNet-B0 achieving the best computational efficiency suitable for real-time industrial deployment. This approach shows the effectiveness of realistic synthetic data for robust CNN training, which offers significant potential for automated industrial PSD monitoring. The code is released at : https://github.com/YasserElj/Synthetic-Granular-Gen