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One-Epoch Training for Object Detection in Fisheye Images

Published: 01 January 2024 Publication History

Abstract

This challenge is divided into two stages: qualification and final competition. We will acquire regular image data and need to perform detection on images with a fisheye effect. The approach described in this context begins by taking the original images and transforming them to mimic fisheye effect images for training. Furthermore, this challenge imposes limitations on computational resources, so striking a balance between accuracy and speed is a crucial aspect. In this paper, we asserted that our approach for this competition can achieve high performance with just one epoch of training. In summary, we achieved the top position among 24 participating teams in the qualification competition and secured the fourth position among the 11 successful submitted teams in the final competition. The corresponding source code will be available at: One-Epoch Training for Object Detection in Fisheye Images.

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cover image ACM Conferences
MMAsia '23: Proceedings of the 5th ACM International Conference on Multimedia in Asia
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 01 January 2024

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Author Tags

  1. Fisheye Images
  2. Object Detection
  3. One-Epoch Training
  4. YOLOv7

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MMAsia '23
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MMAsia '23: ACM Multimedia Asia
December 6 - 8, 2023
Tainan, Taiwan

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Overall Acceptance Rate 59 of 204 submissions, 29%

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