Vision Transformer-based Real-Time Camouflaged Object Detection System at Edge

dc.contributor.authorPutatunda, Rohan
dc.contributor.authorKhan, Md Azim
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorWang, Jianwu
dc.contributor.authorBusart, Carl
dc.contributor.authorErbacher, Robert F.
dc.date.accessioned2023-08-21T22:40:29Z
dc.date.available2023-08-21T22:40:29Z
dc.date.issued2023-08-07
dc.description2023 IEEE International Conference on Smart Computing (SMARTCOMP), 26-30 June 2023, Nashville, TN, USAen_US
dc.description.abstractCamouflaged object detection is a challenging task in computer vision that involves identifying objects that are intentionally or unintentionally hidden in their surrounding environment. Vision Transformer mechanisms play a critical role in improving the performance of deep learning models by focusing on the most relevant features that help object detection under camouflaged conditions. In this paper, we utilized a vision transformer (VT) in two phases, a) By integrating VT with a deep learning architecture for efficient monocular depth map generation for camouflaged objects and b) By embedding VT multiclass object detection model with multimodal feature input (RGB with RGB-D) that increases the visual cues and provides more representational information to the model for performance enhancement. Additionally, we performed an ablation study to understand the role of the vision transformer in camouflaged object detection and incorporated GRAD-CAM on top of the model to visualize the performance improvement achieved by embedding the VT in the model architecture. We deployed the model on resource-constrained edge devices for real-time object detection to realistically test the performance of the trained model.en_US
dc.description.sponsorshipThis work is supported by U.S. Army Grant No: W911NF2120076en_US
dc.description.urihttps://ieeexplore.ieee.org/document/10207675en_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2joip-qdr4
dc.identifier.citationR. Putatunda, M. A. Khan, A. Gangopadhyay, J. Wang, C. Busart and R. F. Erbacher, "Vision Transformer-based Real-Time Camouflaged Object Detection System at Edge," 2023 IEEE International Conference on Smart Computing (SMARTCOMP), Nashville, TN, USA, 2023, pp. 90-97, doi: 10.1109/SMARTCOMP58114.2023.00029.en_US
dc.identifier.urihttps://doi.org/10.1109/SMARTCOMP58114.2023.00029
dc.identifier.urihttp://hdl.handle.net/11603/29310
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleVision Transformer-based Real-Time Camouflaged Object Detection System at Edgeen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Vision_Transformer-based_Real-Time_Camouflaged_Object_Detection_System_at_Edge.pdf
Size:
1.81 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description: