Object Detection with Transformers: A Review

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Transformers have revolutionized Natural Language Processing (NLP).
• Detection Transformer (DETR) applies transformers to object detection, framing it as a set prediction problem.
• Initial DETR versions faced challenges with slow convergence and detecting small objects.

Purpose of the Study:

• To provide a comprehensive review of 25 recent advancements in Detection Transformers (DETR).
• To analyze foundational DETR modules and recent enhancements.
• To encourage further research in transformer-based object detection.

Main Methods:

• Review of 25 recent DETR advancements.
• Analysis of modifications to backbone structure, query design, and attention mechanisms.
• Comparative analysis of various detection transformers' performance and architectures.

Main Results:

• Numerous improvements have addressed DETR's initial limitations, leading to state-of-the-art performance.
• DETR advancements include backbone modifications, refined query strategies, and improved attention mechanisms.
• Comparative analysis highlights performance and architectural differences across various DETR models.

Conclusions:

• DETR has evolved significantly, overcoming initial performance hurdles.
• This review offers a structured overview of DETR's progress and challenges.
• Future research directions are identified to further advance transformer applications in object detection.