Quantum-Soft QUBO Suppression for Accurate Object Detection

Junde Li, Swaroop Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades. It eliminates false positives by only keeping the image M with highest detection score and images whose overlap ratio with M is less than a predefined threshold. However, this greedy algorithm may not work well for object detection under occlusion scenario where true positives with lower detection scores are possibly suppressed. In this paper, we first map the task of removing redundant detections into Quadratic Unconstrained Binary Optimization (QUBO) framework that consists of detection score from each bounding box and overlap ratio between pair of bounding boxes. Next, we solve the QUBO problem using the proposed Quantum-soft QUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages. Experiments indicate that QSQS improves mean average precision from 74.20% to 75.11% for PASCAL VOC 2007. It consistently outperforms NMS and soft-NMS for Reasonable subset of benchmark pedestrian detection CityPersons.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030585259
StatePublished - 2020
Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: Aug 23 2020Aug 28 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12374 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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