ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2011.08061
  4. Cited By
FRDet: Balanced and Lightweight Object Detector based on Fire-Residual
  Modules for Embedded Processor of Autonomous Driving

FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving

16 November 2020
Seontaek Oh
Jihwan You
Young-Keun Kim
    ObjD
ArXiv (abs)PDFHTML

Papers citing "FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving"

1 / 1 papers shown
Developing a Compressed Object Detection Model based on YOLOv4 for
  Deployment on Embedded GPU Platform of Autonomous System
Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System
Issac Sim
Junho Lim
Young-Wan Jang
Jihwan You
Seontaek Oh
Young-Keun Kim
164
8
0
01 Aug 2021
1
Page 1 of 1