Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (8): 1-12.DOI: 10.3778/j.issn.1002-8331.2210-0063

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Cross-Domain Object Detection Algorithms Based on Depth Domain Adaptation

LIU Hualing, PI Changpeng, ZHAO Chenyu, QIAO Liang   

  1. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China
  • Online:2023-04-15 Published:2023-04-15



  1. 上海对外经贸大学 统计与信息学院,上海 201620

Abstract: In recent years, the object detection algorithm based on deep learning has attracted wide attention due to its high detection performance. It has been successfully applied in many fields such as automatic driving and human-computer interaction and has achieved certain achievements. However, traditional deep learning methods are based on the assumption that the training set (source domain) and the test set (target domain) follow the same distribution, but this assumption is not realistic, which severely reduces the generalization performance of the model. How to align the distribution of the source domain and the target domain so as to improve the generalization of the object detection model has become a research hotspot in the past two years. This article reviews cross-domain object detection algorithms. First, it introduces the preliminary knowledge of cross-domain object detection:depth domain adaptation and object detection. The cross-domain object detection is decomposed into two small areas for an overview, in order to understand its development from the bottom logic. In turn, this article introduces the latest developments in cross-domain object detection algorithms, from the perspectives of differences, confrontation, reconstruction, hybrid and other five categories, and sorts out the research context of each category. Finally, this article summarizes and looks forward to the development trend of cross-domain object detection algorithms.

Key words: deep learning, object detection, deep domain adaptation, cross-domain object detection

摘要: 近年来,基于深度学习的目标检测算法在自动驾驶、人机交互等众多域上有着成功的应用,且因其检测性能较高引起学者的广泛关注。传统的深度学习方法一般基于源域与目标域服从同一分布的假设,但该假设不具备现实性,严重地降低了模型的泛化性能。如何对齐源域与目标域的分布,提高目标检测模型的泛化性成为近两年的研究热点。对跨域目标检测算法进行了综述,介绍了跨域目标检测的预备知识:深度域适应和目标检测,将跨域目标检测分解为两个子问题进行了概述,从底层逻辑理解其发展进程;给出了跨域目标检测算法的最新进展,从差异、对抗、重构、混合和其他等几个分类角度切入,梳理了每个类别的研究脉络并对比了在不同数据集上的性能;通过对目前跨域目标检测算法的梳理和总结,就其未来的研究方向进行展望。

关键词: 深度学习, 目标检测, 深度域适应, 跨域目标检测