# Streamlining the Development of Active Learning Methods in Real-World Object Detection

## TL;DR

This paper introduces object-based set similarity (OSS), a novel metric that predicts active learning method effectiveness and selects robust validation sets in real-world object detection, reducing computational costs and improving evaluation reliability.

## Contribution

The paper presents OSS, a detector-agnostic similarity metric that eliminates ineffective AL methods before training and enhances validation set selection for more reliable evaluation.

## Key findings

- OSS accurately predicts AL method effectiveness without detector training
- OSS enables selection of representative validation sets for robust evaluation
- Validated on three autonomous driving datasets with multiple detectors

## Abstract

Active learning (AL) for real-world object detection faces computational and reliability challenges that limit practical deployment. Developing new AL methods requires training multiple detectors across iterations to compare against existing approaches. This creates high costs for autonomous driving datasets where the training of one detector requires up to 282 GPU hours. Additionally, AL method rankings vary substantially across validation sets, compromising reliability in safety-critical transportation systems. We introduce object-based set similarity ($\mathrm{OSS}$), a metric that addresses these challenges. $\mathrm{OSS}$ (1) quantifies AL method effectiveness without requiring detector training by measuring similarity between training sets and target domains using object-level features. This enables the elimination of ineffective AL methods before training. Furthermore, $\mathrm{OSS}$ (2) enables the selection of representative validation sets for robust evaluation. We validate our similarity-based approach on three autonomous driving datasets (KITTI, BDD100K, CODA) using uncertainty-based AL methods as a case study with two detector architectures (EfficientDet, YOLOv3). This work is the first to unify AL training and evaluation strategies in object detection based on object similarity. $\mathrm{OSS}$ is detector-agnostic, requires only labeled object crops, and integrates with existing AL pipelines. This provides a practical framework for deploying AL in real-world applications where computational efficiency and evaluation reliability are critical. Code is available at https://mos-ks.github.io/publications/.