Low-Light Detector Based on Feature Filtering and Enhancement

Xinyu Wang, Baoguo Wei, Yuetong Su, Xu Li, Lixin Li

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

Abstract

Low-light scenarios pose a challenge for object detection due to noise and low brightness. The degraded feature maps are the main factors affecting detection performance. To improve the quality of low-light feature maps, we propose double adaptive filters (DAF) and dark feature pyramid network (DFPN). DAF enhances feature representation by filtering and enhancement. Applying the adaptive kernel adjustment to the filters effectively improves interactions in the current dimension. Due to the limited detail information in low-light feature maps, the DFPN is proposed to compensate for the details. We integrate these two modules into the advanced YOLOX detector. We achieve state-of-the-art performance with unloaded and loaded COCO weights, achieving 69.1% and 83.2% accuracy on the ExDark dataset.

Original languageEnglish
Title of host publication2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings
EditorsBhaskar D Rao, Isabel Trancoso, Gaurav Sharma, Neelesh B. Mehta
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350368741
DOIs
StatePublished - 2025
Event2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Hyderabad, India
Duration: 6 Apr 202511 Apr 2025

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Country/TerritoryIndia
CityHyderabad
Period6/04/2511/04/25

Keywords

  • Filtering
  • Low Light
  • Object Detection

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