Reference : Poster: Lightweight Features Sharing for Real-Time Object Detection in Cooperative Driving
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/54850
Poster: Lightweight Features Sharing for Real-Time Object Detection in Cooperative Driving
English
Hawlader, Faisal mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Engel >]
Robinet, François mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >]
Frank, Raphaël mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >]
26-Apr-2023
2023 IEEE Vehicular Networking Conference (VNC)
Yes
2023 IEEE Vehicular Networking Conference (VNC)
26-04-2023 to 28-04-2023
Istanbul
Türkiye
[en] Real-time Object Detection ; Neural Network Quantization ; Model Compression ; V2X Communication
[en] In model partitioning for real-time object detection, part of the model is deployed on a vehicle, and the remaining layers are processed in the cloud. Model partitioning requires transmitting intermediate features to the cloud, which can be problematic, given that the latency requirements are strict. This paper addresses this issue by demonstrating a lightweight featuresharing strategy while investigating a trade-off between detection quality and latency. We report details on layer partitioning, such as which layers to split in order to achieve the desired accuracy.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/54850

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