A Review of DEtection TRansformer: From Basic Architecture to Advanced Developments and Visual Perception Applications

A Review of DEtection TRansformer: From Basic Architecture to Advanced Developments and Visual Perception Applications

DEtection TRansformer (DETR) introduced an end-to-end object detection paradigm using Transformers, eliminating hand-crafted components like anchor boxes and Non-Maximum Suppression (NMS) via set prediction and bipartite matching. Despite its potential, the original DETR suffered from slow convergen...

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Bibliographic Details
Main Authors: Liang Yu, Lin Tang, Lisha Mu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
Subjects:
object detection
DETR
transformer
attention
end to end
deep learning
Online Access:https://www.mdpi.com/1424-8220/25/13/3952
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Summary:DEtection TRansformer (DETR) introduced an end-to-end object detection paradigm using Transformers, eliminating hand-crafted components like anchor boxes and Non-Maximum Suppression (NMS) via set prediction and bipartite matching. Despite its potential, the original DETR suffered from slow convergence, poor small object detection, and low efficiency, prompting extensive research. This paper systematically reviews DETR’s technical evolution from a “problem-driven” perspective, focusing on advancements in attention mechanisms, query design, training strategies, and architectural efficiency. We also outline DETR’s applications in autonomous driving, medical imaging, and remote sensing, and its expansion to fine-grained classification and video understanding. Finally, we summarize current challenges and future directions. This “problem-driven” analysis offers researchers a comprehensive and insightful overview, aiming to fill gaps in the existing literature on DETR’s evolution and logic.
ISSN:1424-8220

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