# Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario

Source: [Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario](<https://www.sota2.com/research/paper/68fa6a211e209131cea26281>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:07:38.365Z

Authors: Hooman Tavakoli, Snehal Walunj, Parsha Pahlevannejad, Christiane Plociennik, Martin Ruskowski

Published: 2021-06-11T13:59:44.000Z

## Abstract

Detecting small objects in video streams of head-worn augmented reality devices in near real-time is a huge challenge: training data is typically scarce, the input video stream can be of limited quality, and small objects are notoriously hard to detect. In industrial scenarios, however, it is often possible to leverage contextual knowledge for the detection of small objects. Furthermore, CAD data of objects are typically available and can be used to generate synthetic training data. We describe a near real-time small object detection pipeline for egocentric perception in a manual assembly scenario: We generate a training data set based on CAD data and realistic backgrounds in Unity. We then train a YOLOv4 model for a two-stage detection process: First, the context is recognized, then the small object of interest is detected. We evaluate our pipeline on the augmented reality device Microsoft Hololens 2.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.06403>)

[PDF](<https://arxiv.org/pdf/2106.06403.pdf>)