Improving Multimodal Object Detection with Individual Sensor Monitoring

Christopher B. Kuhn, Markus Hofbauer, Ma Bowen, Goran Petrovic, Eckehard Steinbach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multimodal object detection fuses different sensors such as camera or LIDAR to improve the detection performance. However, individual sensor inputs can also be detrimental to a system, for example when sun glare hits a camera. In this work, we propose to monitor each sensor individually to predict when an input would lead to incorrect detections. We first train one detection network for each sensor separately, using only that sensor as input. Then, we record the performance for each single-sensor network and train an introspective performance prediction network for each sensor. Finally, we train a multimodal fusion network where we weight the impact of each sensor with its predicted performance. This allows us to dynamically adapt the fusion to reduce the influence of harmful sensor readings based only on the current data. We apply the proposed concept to the state-of-the-art AVOD architecture and evaluate on the KITTI data set. The proposed sensor monitoring system improves the mean intersection-over-union performance by 4.6%. For inputs with a low predicted performance, the proposed approach outperforms the state of the art by over 10%, demonstrating the potential of using individual sensor monitoring to react to problematic input. The proposed approach can be applied to any fusion network with two or more sensors and could also be used for classification or segmentation tasks.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Symposium on Multimedia, ISM 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-104
Number of pages8
ISBN (Electronic)9781665471725
DOIs
StatePublished - 2022
Event24th IEEE International Symposium on Multimedia, ISM 2022 - Virtual, Online, Italy
Duration: 5 Dec 20227 Dec 2022

Publication series

NameProceedings - 2022 IEEE International Symposium on Multimedia, ISM 2022

Conference

Conference24th IEEE International Symposium on Multimedia, ISM 2022
Country/TerritoryItaly
CityVirtual, Online
Period5/12/227/12/22

Keywords

  • Introspection
  • Object Detection
  • Sensor Fusion

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