A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection
2021
Electronics
Preserved Fulltext
Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. ...
To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. ...
The proposed underwater object detection CNN network combined U-Net and MobileNetV3-SSDLite is successfully implemented into the FPGA of our underwater robot, where it can achieve real-time object detection ...
doi:10.3390/electronics10232889
fatcat:7p6woc4g6jc6tozls7ivi5h2py
Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors
2020
IEEE transactions on circuits and systems for video technology (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2009.01588v1 fatcat:4myj62kf4je3pawxlytdrt2f6i
The model size is reduced by 22.66-28.93 times compared to that in a full-precision network with a negligible degradation of accuracy on VOC, COCO, and ImageNet datasets. ...
The mixed data flow aims to minimize the off-chip access while demanding a minimal on-chip memory (BRAM) resource of an FPGA device. ...
For achieving real-time operation, numerous FPGA designs are available for a YOLO CNN [7] - [11] . The previous designs in [7] , [10] , and [11] achieve a real-time throughput. ...
doi:10.1109/tcsvt.2020.3020569
fatcat:wdypzy6bufhuzj7vcs7arx5wem
2021 Index IEEE Transactions on Very Large Scale Integration (VLSI) Systems Vol. 29
2021
IEEE Transactions on Very Large Scale Integration (vlsi) Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Lyu, F., +, TVLSI July 2021 1470-1474 Real-Time SSDLite Object Detection on FPGA. ...
., +, TVLSI Feb. 2021 333-346 IMCA: An Efficient In-Memory Convolution Accelerator. Yantir, H.E., +, TVLSI March 2021 447-460 Real-Time SSDLite Object Detection on FPGA. ...
doi:10.1109/tvlsi.2021.3136367
fatcat:fwqswbyzejgfhgbzywrvsf2qgi
On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems
2021
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. ...
Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based ...
Healthcare
-
-
[56]
2019
Vehicle and
pedestrian detection
Generic
-
•
Real-time execution
•
High accuracy
[57]
2019
Object detection in
UAV imagery
Generic
-
•
Real-time execution ...
doi:10.3390/app11199173
fatcat:cncucjelmrgv3mdortgxax2qly
Cracking open the DNN black-box
2019
Proceedings of the 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges - HotEdgeVideo'19
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Despite rapid advances in system design, existing systems treat DNNs largely as "black boxes" and either deploy models entirely on a camera or compress videos for analysis in the cloud. ...
We present promising results from preliminary work in efficiently encoding the intermediate activations sent between layers of a neural network and describe opportunities for further research. ...
Modern DNNs, however, require special purpose hardware accelerators (e.g. GPUs, FPGAs [23], TPUs [24] ) to run in real time (≥ 30 frames/sec) on HD videos (≥ 720p resolution). ...
doi:10.1145/3349614.3356023
dblp:conf/mobicom/EmmonsFAVSW19
fatcat:z4psoigzdra4jlqtgskx5xbhga
FPGA Based Embedded Neural Network Object Detector
2021
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Since FPGAs provide high flexibility in combination with low power consumption, FPGAs are a promising candidate for implementing a real-time capable object detector for edge devices.Therefore, this work ...
However, the enormous computational effort that CPUs cannot handle in real-time is a crucial disadvantage of object detection based on deep learning compared to conventional algorithms. ...
the given real-time object detection task. ...
doi:10.34726/hss.2021.69314
fatcat:becnaaulgze4pf7hmkba7lmfni