基于UNet的干式磁选分选颗粒轨迹在线检测识别方法
Method for online detection and recognition of particle trajectories in dry magnetic separation based on UNet
针对干式磁选分选过程中颗粒形状、大小和颜色等特征多样性所带来的颗粒轨迹检测和识别难点，提出了一种基于UNet的颗粒轨迹在线检测识别方法。该方法采用深度学习技术对干式磁选分选过程的图像序列进行处理，快速识别颗粒轨迹，并提取分析颗粒特征。实验结果表明，该方法的识别准确率高达93.5%上，有效提高了干式磁选分选过程的效率和精度，降低了人工操作的成本和误差率。该方法还为干式磁选分选过程的精细化管理提供了技术支持。其现实意义在于提高干式磁选分选过程的自动化…查看全部>>
The article proposes a UNet-based online detection and recognition method for particle trajectories to address the challenges of detecting and identifying particle trajectories in dry magnetic separation processes due to the diversity of particle characteristics, such as shape, size, and color. The method processes image sequences in dry magnetic separation processes using deep learning technology to quickly identify particle trajectories and extract and ana…查看全部>>
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