  The astounding performance of transformers in natural language processing
(NLP) has motivated researchers to explore their applications in computer
vision tasks. DEtection TRansformer (DETR) introduces transformers to object
detection tasks by reframing detection as a set prediction problem.
Consequently, eliminating the need for proposal generation and post-processing
steps. Initially, despite competitive performance, DETR suffered from slow
training convergence and ineffective detection of smaller objects. However,
numerous improvements are proposed to address these issues, leading to
substantial improvements in DETR and enabling it to exhibit state-of-the-art
performance. To our knowledge, this is the first paper to provide a
comprehensive review of 21 recently proposed advancements in the original DETR
model. We dive into both the foundational modules of DETR and its recent
enhancements, such as modifications to the backbone structure, query design
strategies, and refinements to attention mechanisms. Moreover, we conduct a
comparative analysis across various detection transformers, evaluating their
performance and network architectures. We hope that this study will ignite
further interest among researchers in addressing the existing challenges and
exploring the application of transformers in the object detection domain.
Readers interested in the ongoing developments in detection transformers can
refer to our website at:
https://github.com/mindgarage-shan/trans_object_detection_survey
