Object Detection and Recognition in Low Light Image Using Deep Learning Techniques

dc.contributor.authorBasim Jasim, Fatima
dc.contributor.authorHasoon Khayeat, Ali Retha
dc.contributor.editorSkala, Václav
dc.date.accessioned2025-07-30T10:36:29Z
dc.date.available2025-07-30T10:36:29Z
dc.date.issued2025
dc.description.abstract-translatedObject detection in low-light conditions remains a critical challenge for applications in surveillance, autonomous navigation, security, etc. were poor image visibility results in poor detection accuracy especially in very low-light images. This paper presents an analysis of two popular object detection frameworks YOLOv9 and Faster R-CNN on low-light images after enhancing them using optimization techniques. We implemented a Proposed Method technique consisting of a set of image enhancement steps. Our results indicate that YOLOv9 and Faster R-CNN achieve superior performance with higher average recall, precision, and mAP. These results confirm the impact of integrating low-light enhancement methods on the detection accuracy in dealing with the complexities of low-light image environments. This paper contributes to the development of an image enhancement system for low-light conditions and the pursuit of better results in object detection in imagesen
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.doihttp://www.doi.org/10.24132/CSRN.2025-32
dc.identifier.issn2464-4617 (Print)
dc.identifier.issn2464-4625 (online)
dc.identifier.urihttp://hdl.handle.net/11025/62242
dc.language.isoenen
dc.publisherVaclav Skala - UNION Agencyen
dc.rights© Vaclav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectdetekce objektůcs
dc.subjectvylepšení obrazucs
dc.subjectCNNcs
dc.subjectexdark datasetcs
dc.subjecttmavé obrazycs
dc.subject.translatedobject detectionen
dc.subject.translatedimage enhancementen
dc.subject.translatedCNNen
dc.subject.translatedexdark dataseten
dc.subject.translateddark imagesen
dc.titleObject Detection and Recognition in Low Light Image Using Deep Learning Techniquesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count1*
local.files.size1420389*
local.has.filesyes*

Files

Original bundle
Showing 1 - 1 out of 1 results
No Thumbnail Available
Name:
A17.pdf
Size:
1.35 MB
Format:
Adobe Portable Document Format
License bundle
Showing 1 - 1 out of 1 results
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: