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English(EN) Real-Time Conveyor Belt Defect Detection via Edge TPU-Accelerated YOLOv11 on Raspberry Pi 5

边缘AI系统在Raspberry Pi 5上使用YOLOv11进行实时输送带缺陷检测

研究人员开发了一个用于输送带的实时缺陷检测系统,该系统在配备Google Coral Edge TPU的Raspberry Pi 5上部署了优化的YOLOv11 nano模型。这种低功耗的边缘AI解决方案旨在解决传统依赖GPU的模型在工业环境中的局限性。该系统在来自运营中的水泥厂和开源数据集的3000多个增强样本上进行训练,实现了0.742的精确率和0.730的召回率。它在低延迟的情况下实现了30-40 FPS的稳定吞吐量,证明了其在恶劣工业环境中进行预测性维护的有效性。 AI

影响 通过将先进的计算机视觉技术引入边缘设备,实现了工业环境中具有成本效益的实时预测性维护。

排序理由 这是一篇研究论文,详细介绍了现有AI模型和硬件在特定工业问题上的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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边缘AI系统在Raspberry Pi 5上使用YOLOv11进行实时输送带缺陷检测

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这是一篇研究论文,详细介绍了现有AI模型和硬件在特定工业问题上的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Muhammad Nur Bijak Bestari, Mulki Indana Zulfa ·

    基于 Raspberry Pi 5 上 Edge TPU 加速的 YOLOv11 实现的实时输送带缺陷检测

    arXiv:2610.34304v1 Announce Type: new Abstract: Conveyor belts are vital arteries in continuous bulk material handling, particularly in heavy process industries such as cement manufacturing. Undetected structural anomalies (longitudinal tears, holes, degraded patch repairs, and i…