标题： 柔性制造中的鲁棒组件检测：一种用于可变光照下无托盘物体识别的深度学习方法 显示英文标题

标题： Robust Component Detection for Flexible Manufacturing: A Deep Learning Approach to Tray-Free Object Recognition under Variable Lighting

摘要： 柔性制造系统在第四次工业革命中需要能够在非结构化环境中操作物体且不受严格定位约束的机器人。 本文提出了一种计算机视觉系统，使工业机器人能够在不使用结构化托盘的情况下检测和抓取任意方向的笔部件，同时在不同光照条件下保持强大的性能。 我们在ZHAW的一条完整的笔制造线上实现了并评估了一种基于Mask R-CNN的方法，解决了三个关键挑战：无位置约束的目标检测、对极端光照变化的鲁棒性以及使用成本效益相机的可靠性能。 我们的系统在多种光照条件下实现了95%的检测准确率，同时消除了对结构化组件放置的需求，展示了设置时间减少了30%，显著提高了制造灵活性。 该方法通过四种不同的光照场景进行了广泛测试，证明了其在实际工业部署中的实用性。 显示英文摘要

摘要： 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.