无需固定托盘，光照变化下仍能精准识别笔组件。
Robust Component Detection for Flexible Manufacturing: A Deep Learning Approach to Tray-Free Object Recognition under Variable Lighting
- 基于Mask R-CNN的视觉系统，实现无位置约束检测
- 光照多变环境下检测准确率达95%，节省30%部署时间
- 适合工业现场快速部署，提升柔性制造灵活性
工业4.0中的柔性制造系统要求机器人在非结构化环境中处理任意朝向的物体，而无需依赖固定托盘。本文提出一种计算机视觉系统，使工业机器人可在不同光照条件下检测并抓取笔组件，且不需结构化摆放。我们在ZHAW的完整笔制造产线中实现了基于Mask R-CNN的方法，解决了三大挑战：无定位约束的物体检测、极端光照变化下的鲁棒性、以及使用低成本相机的可靠性能。系统在四种不同光照场景下均达到95%的检测准确率，消除了对结构化放置的需求，使设置时间减少30%，显著提升了制造灵活性。该方法通过大量测试验证，具备实际工业部署可行性。
原文摘要 · Abstract (English)
Flexible manufacturing systems in Industry 4.0 require robots capable of handling objects in unstructured environments without rigid positioning constraints. This paper presents a computer vision system that enables industrial robots to detect and grasp pen components in arbitrary orientations without requiring structured trays, while maintaining robust performance under varying lighting conditions. We implement and evaluate a Mask R-CNN-based approach on a complete pen manufacturing line at ZHAW, addressing three critical challenges: object detection without positional constraints, robustness to extreme lighting variations, and reliable performance with cost-effective cameras. Our system achieves 95% detection accuracy across diverse lighting conditions while eliminating the need for structured component placement, demonstrating a 30% reduction in setup time and significant improvement in manufacturing flexibility. The approach is validated through extensive testing under four distinct lighting scenarios, showing practical applicability for real-world industrial deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。