  Object detection is an important topic in computer vision, with
post-processing, an essential part of the typical object detection pipeline,
posing a significant bottleneck affecting the performance of traditional object
detection models. The detection transformer (DETR), as the first end-to-end
target detection model, discards the requirement of manual components like the
anchor and non-maximum suppression (NMS), significantly simplifying the target
detection process. However, compared with most traditional object detection
models, DETR converges very slowly, and a query's meaning is obscure. Thus,
inspired by the Step-by-Step concept, this paper proposes a new two-stage
object detection model, named DETR with YOLO (DEYO), which relies on a
progressive inference to solve the above problems. DEYO is a two-stage
architecture comprising a classic target detection model and a DETR-like model
as the first and second stages, respectively. Specifically, the first stage
provides high-quality query and anchor feeding into the second stage, improving
the performance and efficiency of the second stage compared to the original
DETR model. Meanwhile, the second stage compensates for the performance
degradation caused by the first stage detector's limitations. Extensive
experiments demonstrate that DEYO attains 50.6 AP and 52.1 AP in 12 and 36
epochs, respectively, while utilizing ResNet-50 as the backbone and multi-scale
features on the COCO dataset. Compared with DINO, an optimal DETR-like model,
the developed DEYO model affords a significant performance improvement of 1.6
AP and 1.2 AP in two epoch settings.
