Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (1): 37-48.DOI: 10.3778/j.issn.1002-8331.2205-0354

• Research Hotspots and Reviews • Previous Articles     Next Articles

Object Detection Algorithms Based on Deep Learning and Transformer

FU Miaomiao, DENG Miaolei, ZHANG Dexian   

  1. 1.College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
    2.Henan Province Grain Information Processing International Joint Laboratory, Zhengzhou 450001, China
  • Online:2023-01-01 Published:2023-01-01



  1. 1.河南工业大学 信息科学与工程学院,郑州 450001
    2.河南省粮食信息处理国际联合实验室,郑州 450001

Abstract: Object detection is the basis for advanced vision tasks such as object tracking and instance segmentation, and has important applications in real-world scenarios such as intelligent transportation, defect detection, and intelligent security. Existing high-precision detection algorithms are all implemented under the guidance of deep learning, accompanied by Anchor frame technology. However, the shortcomings of the anchor frame itself have a great impact on the performance of the detector. Anchor-free collision detection has become a target detection method in recent years. new research directions in the field. At the same time, the great potential shown by Transformer has opened up a new direction of combining image and Transformer for the field of vision, and Transformer-based target detection has also become a new research hotspot. This paper systematically summarizes the target detection algorithms in the deep learning era, investigates and studies related papers on target detection in the past five years, focuses on in-depth analysis of these algorithms from the perspectives of Anchor-free and Transformer, and introduces the specific application situation of these algorithms in real scenarios and the commonly used datasets in the field of target detection. Finally, based on the current research status, the future research directions of target detection are prospected.

Key words: computer vision, object detection, Anchor-free detection, Transformer

摘要: 目标检测是实现目标跟踪、实例分割等高级视觉任务的基础,在智慧交通、缺陷检测、智能安防等现实场景有着重要应用。现有高精度检测算法都是在深度学习的指导下实现,同时伴有锚框技术,但是锚框自身的不足对检测器性能有着较大影响,无锚点碰撞检测成为了近几年目标检测领域新的研究方向。与此同时,Transformer表现出的巨大潜力为视觉领域开辟了图像与Transformer结合这个新方向,基于Transformer的目标检测也成为一个新的研究热点。系统地总结了深度学习时代的目标检测算法,调查并研究了近五年目标检测的相关论文,重点从Anchor-free和Transformer两个角度对这些算法进行深入分析,介绍了这些算法在现实场景具体应用情况以及目标检测领域常用数据集,基于目前的研究现状对目标检测的未来可研究方向进行了展望。

关键词: 计算机视觉, 目标检测, 无锚检测, Transformer