Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
IR Reasoner: Real-time Infrared Object Detection by Visual Reasoning
Date
2023-01-01
Author
Gündoǧan, Meryem Mine
Aksoy, Tolga
Temizel, Alptekin
Halıcı, Uğur
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
10
views
0
downloads
Cite This
Thermal Infrared (IR) imagery is utilized in several applications due to their unique properties. However, there are a number of challenges, such as small target objects, image noise, lack of textural information, and background clutter, negatively affecting detection of objects in IR images. Current real-time object detection methods treat each image region separately and, in face of these challenges, this sole dependency on feature maps extracted by convolutional layers is not ideal. In this paper, we introduce a new architecture for real-time object detection in IR images by reasoning the relations between image regions by using self-attention. The proposed method, IR Reasoner, takes the spatial and semantic coherency between image regions into account to enhance the feature maps. We integrated this approach into the current state-of-the-art one-stage object detectors YOLOv4, YOLOR, and YOLOv7, and trained them from scratch on the FLIR ADAS dataset. Experimental evaluations show that the Reasoner variants perform better than the baseline models while still running in real-time. Our best performing Reasoner model YOLOv7-W6-Reasoner achieves 40.5% AP at 32.7 FPS. The code is publicly available.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85170825959&origin=inward
https://hdl.handle.net/11511/107291
DOI
https://doi.org/10.1109/cvprw59228.2023.00048
Conference Name
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
Collections
Graduate School of Informatics, Conference / Seminar
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. M. Gündoǧan, T. Aksoy, A. Temizel, and U. Halıcı, “IR Reasoner: Real-time Infrared Object Detection by Visual Reasoning,” Vancouver, Kanada, 2023, vol. 2023-June, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85170825959&origin=inward.