Review of DETR Object Detection Algorithm Based on Transformer

Convolutional Neural Networks (CNNs) are widely used in the field of object detection, earning widespread acclaim in scholarly circles due to their precision and scalability. It has spawned numerous notable models, including those in the Region-based Convolutional Neural Networks (R-CNNs) (such as Fast R-CNN and Faster R-CNN) and You Only Look Once (YOLO) series. After the success of Transformers in the field of natural language processing, researchers began exploring their application in computer vision, leading to the development of visual backbone networks such as Visual Transformer (ViT) and Swin Transformer. In 2020, a Facebook research team unveiled DEtection TRansformer (DETR), an end-to-end object detection algorithm based on Transformers, designed to minimize the need for prior knowledge and postprocessing in object detection tasks. Despite the promise shown by DETR in object detection, it has limitations including low convergence speed, relatively low accuracy, and the ambiguous physical significance of target queries. These issues have spurred a wave of research aimed at refining and enhancing the algorithm. This paper aims to collate, scrutinize, and synthesize the various efforts aimed at improving DETR, assessing their respective merits and demerits. Furthermore, it presents a comprehensive overview of state-of-the-art research and specialized application domains that employ DETR and concludes with a prospective analysis of the future role of DETR in the field of computer vision.