用少量摄像头+机器学习实现装配流程高精度阶段验证
Learning-based Stage Verification System in Manual Assembly Scenarios
- 融合多模型与同步状态信息，实现装配阶段精准识别
- 平均准确率超92%，支持实时错误检测与可视化引导
- 适合需低成本部署的智能工厂装配场景
在工业4.0背景下，对装配过程中多个目标和状态的高效监控至关重要，尤其受限于仅使用视觉传感器时。传统方法常依赖多种传感器或复杂硬件以保证高精度，但成本高昂且难适应动态工业环境。本研究提出一种新方法，利用多个机器学习模型，在仅使用最少数量视觉传感器的条件下实现精确监控。通过整合相同时间戳的状态信息，该方法可检测并确认装配过程当前阶段，平均准确率超过92%。此外，该方法在误差检测与可视化方面优于传统手段，为操作员提供实时、可操作的指导，不仅提升了装配监控的准确性与效率，还降低了对昂贵硬件的依赖，更适合现代工业应用。
原文摘要 · Abstract (English)
In the context of Industry 4.0, effective monitoring of multiple targets and states during assembly processes is crucial, particularly when constrained to using only visual sensors. Traditional methods often rely on either multiple sensor types or complex hardware setups to achieve high accuracy in monitoring, which can be cost-prohibitive and difficult to implement in dynamic industrial environments. This study presents a novel approach that leverages multiple machine learning models to achieve precise monitoring under the limitation of using a minimal number of visual sensors. By integrating state information from identical timestamps, our method detects and confirms the current stage of the assembly process with an average accuracy exceeding 92%. Furthermore, our approach surpasses conventional methods by offering enhanced error detection and visuali-zation capabilities, providing real-time, actionable guidance to operators. This not only improves the accuracy and efficiency of assembly monitoring but also re-duces dependency on expensive hardware solutions, making it a more practical choice for modern industrial applications.
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