IAdet: Simplest human-in-the-loop object detection

TL;DR

This paper introduces IAdet, a simple human-in-the-loop object detection system that reduces annotation time by 25% on PASCAL VOC, combining assisted annotation, background training, and active data selection.

Contribution

It presents a straightforward framework and open-source tool for human-in-the-loop object detection, along with an automatic evaluation method.

Findings

Reduces annotation time by 25% on PASCAL VOC

Provides a trained model for free

Open-source tool for single-class detection

Abstract

This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA). IA involves three modules: (1) assisted data annotation, (2) background model training, and (3) active selection of the next datapoints. Under this framework, we open-source the IAdet tool, which is specific for single-class object detection. Additionally, we devise a method for automatically evaluating such a human-in-the-loop system. For the PASCAL VOC dataset, the IAdet tool reduces the database annotation time by $25\%$ while providing a trained model for free. These results are obtained for a deliberately very simple IAdet design. As a consequence, IAdet is susceptible to multiple easy improvements, paving the way for powerful human-in-the-loop object detection systems.