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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.00852 |
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| _version_ | 1866913921470824448 |
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| author | Daneshmand, Fatemeh Sadat |
| author_facet | Daneshmand, Fatemeh Sadat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Component Detection for Flexible Manufacturing: A Deep Learning Approach to Tray-Free Object Recognition under Variable Lighting Daneshmand, Fatemeh Sadat Computer Vision and Pattern Recognition 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. |
| title | Robust Component Detection for Flexible Manufacturing: A Deep Learning Approach to Tray-Free Object Recognition under Variable Lighting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.00852 |