CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection

Presenti, Alice; Liang, Z.; Pereira, Luis F. Alves; Sijbers, Jan; Beenhouwer, Jan De

doi:10.1109/rtsi50628.2021.9597330

Cited by 1 publication

(1 citation statement)

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“…Methods for 6-DoF pose estimation in the X-ray domain have been proposed for applications ranging from industrial product inspection, C-arm repositioning for surgical assistance, to surgical tool pose estimation. Presenti et al propose a series of methods [30]- [32] to recover manufactured object pose from X-ray images for defect inspection. Their approach assumes fixed acquisition geometry and displays sub-optimal results when only one image is used [32], compared to methods employing PnP.…”

Section: B Object Pose Estimation In X-raymentioning

confidence: 99%

Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries

Viviers,

Filatova,

Termeer

et al. 2024

IEEE Trans. on Image Process.

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Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing deep learning methods have achieved accurate results, but they require custom approaches for each object and laborious setup and training environments often stretching to extensive simulations, whilst lacking real-time computation. We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image. The proposed YOLOv5-6D pose model achieves competitive results on public benchmarks whilst being considerably faster at 42 FPS on GPU. In addition, the method generalizes across varying X-ray acquisition geometry and semantic image complexity to enable accurate pose estimation over different domains. Finally, the proposed approach is tested for bone-screw pose estimation for computer-aided guidance during spine surgeries. The model achieves a 92.41% by the 0.1•d ADD-S metric, demonstrating a promising approach for enhancing surgical precision and patient outcomes. The code for YOLOv5-6D is publicly available at https://github.com/cviviers/YOLOv5-6D-Pose.

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Section: B Object Pose Estimation In X-raymentioning

confidence: 99%