Detection-Driven Exposure-Correction Network for Nighttime Drone-View Object Detection

Xi, Y; Jia, W; Miao, Q; Feng, J; Ren, J; Luo, H

Detection-Driven Exposure-Correction Network for Nighttime Drone-View Object Detection

Xi, Y Jia, W

Miao, Q Feng, J Ren, J Luo, H

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, pp. 1-14
Issue Date:: 2024-01-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Xi, Y
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.contributor.author	Miao, Q
dc.contributor.author	Feng, J
dc.contributor.author	Ren, J
dc.contributor.author	Luo, H
dc.date.accessioned	2024-05-05T00:53:08Z
dc.date.available	2024-05-05T00:53:08Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, pp. 1-14
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/178655
dc.description.abstract	Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, nonlinear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a fine-grained parameter predictor (FPP) to estimate pixelwise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on nonuniform illuminations in drone-captured images. In order to learn the nonlinear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a progressive filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art (SOTA) methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2024.3351134
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	37 Earth sciences
dc.subject.classification	40 Engineering
dc.title	Detection-Driven Exposure-Correction Network for Nighttime Drone-View Object Detection
dc.type	Journal Article
utslib.citation.volume	62
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2026-01-08T00:00:00+1000Z
dc.date.updated	2024-05-05T00:52:57Z
pubs.publication-status	Published
pubs.volume	62

Abstract:

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, nonlinear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a fine-grained parameter predictor (FPP) to estimate pixelwise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on nonuniform illuminations in drone-captured images. In order to learn the nonlinear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a progressive filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art (SOTA) methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset.

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http://hdl.handle.net/10453/178655