Fringe Projection Based Vision Pipeline for Autonomous Hard Drive Disassembly

2026-04-18

AI Summary

This study addresses key challenges in automated hard drive disassembly—namely, incomplete 3D perception, fragmented scene understanding, and difficulty in fastener localization—by proposing a novel vision pipeline based on fringe projection profilometry (FPP). For the first time, FPP is leveraged simultaneously for high-precision depth acquisition and pixel-level aligned instance segmentation. The approach integrates Depth Anything V2 for depth completion, a lightweight real-time instance segmentation network, and a simulation-to-reality (sim-to-real) transfer learning strategy. Experimental results demonstrate exceptional performance: bounding box and mask mAP@50 reach 0.960 and 0.957, respectively; depth completion achieves an RMSE of 2.317 mm and MAE of 1.836 mm; and the entire pipeline operates at only 12.86 ms latency (77.7 FPS), significantly enhancing both geometric and semantic perception accuracy and efficiency.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Unrecovered e-waste represents a significant economic loss. Hard disk drives (HDDs) comprise a valuable e-waste stream necessitating robotic disassembly. Automating the disassembly of HDDs requires holistic 3D sensing, scene understanding, and fastener localization, however current methods are fragmented, lack robust 3D sensing, and lack fastener localization. We propose an autonomous vision pipeline which performs 3D sensing using a Fringe Projection Profilometry (FPP) module, with selective triggering of a depth completion module where FPP fails, and integrates this module with a lightweight, real-time instance segmentation network for scene understanding and critical component localization. By utilizing the same FPP camera-projector system for both our depth sensing and component localization modules, our depth maps and derived 3D geometry are inherently pixel-wise aligned with the segmentation masks without registration, providing an advantage over RGB-D perception systems common in industrial sensing. We optimize both our trained depth completion and instance segmentation networks for deployment-oriented inference. The proposed system achieves a box mAP@50 of 0.960 and mask mAP@50 of 0.957 for instance segmentation, while the selected depth completion configuration with the Depth Anything V2 Base backbone achieves an RMSE of 2.317 mm and MAE of 1.836 mm; the Platter Facing learned inference stack achieved a combined latency of 12.86 ms and a throughput of 77.7 Frames Per Second (FPS) on the evaluation workstation. Finally, we adopt a sim-to-real transfer learning approach to augment our physical dataset. The proposed perception pipeline provides both high-fidelity semantic and spatial data which can be valuable for downstream robotic disassembly. The synthetic dataset developed for HDD instance segmentation will be made publicly available.

Problem

• autonomous disassembly
• hard disk drives
• 3D sensing
• fastener localization
• e-waste

Innovation

• Fringe Projection Profilometry
• instance segmentation
• depth completion
• sim-to-real transfer
• robotic disassembly