Object Detection Based on the DETR Method

DOI

10.2991/978-94-6239-648-7_28How to use a DOI?

Keywords

Object Detection Task; DETR Algorithm; Visual Transformer; Computer Vision

Abstract

One of the computer vision research hotspots is object detection. Its aim is to accurately and quickly identify objects in images and locate their positions, converting visual information into understandable and actionable intelligence. With the success of the Transformer architecture in the field of natural language processing, the Transformer has also been gradually applied to object detection algorithms. DETR was proposed by Facebook as an end-to-end object detection framework. Although DETR shows great potential in the object detection task, it still has limitations such as slow training convergence, relatively weak performance in detecting small objects, and high computational complexity. This has prompted researchers to make improvements and refinements in subsequent works. This article aims to analyze and summarize the evolution stages of DETR, and divides the DETR method into four stages: the pioneering of the DETR method, the efficiency optimization of the DETR method, the improvement of the flexibility of the DETR method, and the breakthrough in the performance of the DETR method. At the same time, representative methods are introduced in each stage. Finally, the prospects of DETR in the object detection task are envisioned.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yiru Wang
PY  - 2026
DA  - 2026/04/24
TI  - Object Detection Based on the DETR Method
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 251
EP  - 259
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_28
DO  - 10.2991/978-94-6239-648-7_28
ID  - Wang2026
ER  -