Object Detection as Probabilistic Set Prediction
Paper i proceeding, 2022

Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems. The development and evaluation of probabilistic object detectors have been hindered by shortcomings in existing performance measures, which tend to involve arbitrary thresholds or limit the detector’s choice of distributions. In this work, we propose to view object detection as a set prediction task where detectors predict the distribution over the set of objects. Using the negative log-likelihood for random finite sets, we present a proper scoring rule for evaluating and training probabilistic object detectors. The proposed method can be applied to existing probabilistic detectors, is free from thresholds, and enables fair comparison between architectures. Three different types of detectors are evaluated on the COCO dataset. Our results indicate that the training of existing detectors is optimized toward non-probabilistic metrics. We hope to encourage the development of new object detectors that can accurately estimate their own uncertainty. Code at https://github.com/georghess/pmb-nll.

Random finite sets

Probabilistic object detection

PMBM

Object detection

Författare

Georg Hess

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Christoffer Petersson

Chalmers, Matematiska vetenskaper, Algebra och geometri

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13670 LNCS XVIII 550-566
978-3-031-20080-9 (ISBN)

17th European Conference on Computer Vision (ECCV)
Tel Aviv, Israel,

Följning av objekt för självkörande fordon med hjälp av djup maskininlärning

Wallenberg AI, Autonomous Systems and Software Program, 2021-08-01 -- 2025-08-01.

Styrkeområden

Transport

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Ämneskategorier

Sannolikhetsteori och statistik

Datorseende och robotik (autonoma system)

DOI

10.1007/978-3-031-20080-9_32

ISBN

9783031200793

Mer information

Senast uppdaterat

2024-09-23