# Snap, Segment, Deploy: A Visual Data and Detection Pipeline for Wearable Industrial Assistants

arXiv: https://arxiv.org/abs/2507.21072 · HTML: https://mlsanity.com/paper/2507.21072/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2025.

## Built on by (most cited first, 6 of 6)

- [MICA: Multi-Agent Industrial Coordination Assistant](https://mlsanity.com/paper/2509.15237/) (2025)
- [IMPACT: A Dataset for Multi-Granularity Human Procedural Action Understanding in Industrial Assembly](https://mlsanity.com/paper/2604.10409/) (2026)
- [IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory](https://mlsanity.com/paper/2604.20136/) (2026)
- [IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction](https://mlsanity.com/paper/2605.01666/) (2026)
- [The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering](https://mlsanity.com/paper/2608.04589/) (2026)
- [INSPECT: Learning Robot View Selection from Assistant Use](https://mlsanity.com/paper/2609.20615/) (2026)

## Abstract (arXiv)

Industrial assembly tasks increasingly demand rapid adaptation to complex procedures and varied components, yet are often conducted in environments with limited computing, connectivity, and strict privacy requirements. These constraints make conventional cloud-based or fully autonomous solutions impractical for factory deployment. This paper introduces a mobile-device-based assistant system for industrial training and operational support, enabling real-time, semi-hands-free interaction through on-device perception and voice interfaces. The system integrates lightweight object detection, speech recognition, and Retrieval-Augmented Generation (RAG) into a modular on-device pipeline that operates entirely on-device, enabling intuitive support for part handling and procedure understanding without relying on manual supervision or cloud services. To enable scalable training, we adopt an automated data construction pipeline and introduce a two-stage refinement strategy to improve visual robustness under domain shift. Experiments on our generated dataset, i.e., Gear8, demonstrate improved robustness to domain shift and common visual corruptions. A structured user study further confirms its practical viability, with positive user feedback on the clarity of the guidance and the quality of the interaction. These results indicate that our framework offers a deployable solution for real-time, privacy-preserving smart assistance in industrial environments. We will release the Gear8 dataset and source code upon acceptance.