What question did this study set out to answer?

The aim is to develop an automated system for accurately detecting and determining the pose of intermeshed steel components using RGB images.

June 20, 2026Machine Vision and ApplicationsOpen Access

ConPose: a jointly trained, single-pass RGB detection-and-pose framework for intermeshed steel connections

View Full Paper

Ask AI

Bookmark

Authors

SASamuel Adebayo DHDavid Hester DMDaniel McPolin

Discussion

Member takes

Overview

Randomized trial evaluates RGB detection and pose accuracy in intermeshed steel connections, suggesting high performance with minimal computational overhead.

Key Points

The aim is to develop an automated system for accurately detecting and determining the pose of intermeshed steel components using RGB images.
Developed a CNN-based detection framework that processes RGB inputs in a single pass.
Utilized a fixed edge bank and class-conditioned feature-wise linear modulation for robustness.
Evaluated performance on a six-class intermeshed steel connections dataset with precise pose annotations.
ConPose achieved mAP of 89.3% at 50 IoU and 71.5% at 75 IoU.
Pose accuracy measured with ADD(S)@0.1 at 73.4% and ADD(S)@0.2 at 88.1%.
Median rotation was 2.0° and median translation was 3.8 cm, outperforming re-trained RGB baselines.

Cite This Study

Adebayo et al. (2026) studied this question.

synapsesocial.com/papers/6a3631fbdb0793dc1a538a65 https://doi.org/10.1007/s00138-026-01863-4

View Full Paper

Ask AI

Bookmark

June 20, 2026Machine Vision and ApplicationsOpen Access

ConPose: a jointly trained, single-pass RGB detection-and-pose framework for intermeshed steel connections

View Full Paper

Ask AI

Bookmark

Authors

SASamuel Adebayo DHDavid Hester DMDaniel McPolin

Discussion

Member takes

Overview

Randomized trial evaluates RGB detection and pose accuracy in intermeshed steel connections, suggesting high performance with minimal computational overhead.

Key Points

The aim is to develop an automated system for accurately detecting and determining the pose of intermeshed steel components using RGB images.
Developed a CNN-based detection framework that processes RGB inputs in a single pass.
Utilized a fixed edge bank and class-conditioned feature-wise linear modulation for robustness.
Evaluated performance on a six-class intermeshed steel connections dataset with precise pose annotations.
ConPose achieved mAP of 89.3% at 50 IoU and 71.5% at 75 IoU.
Pose accuracy measured with ADD(S)@0.1 at 73.4% and ADD(S)@0.2 at 88.1%.
Median rotation was 2.0° and median translation was 3.8 cm, outperforming re-trained RGB baselines.

Cite This Study

Adebayo et al. (2026) studied this question.

synapsesocial.com/papers/6a3631fbdb0793dc1a538a65 https://doi.org/10.1007/s00138-026-01863-4

Also Consider

Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark