Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

Journal of Beijing University of Posts and Telecommunications ›› 2024, Vol. 47 ›› Issue (4): 57-62.

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Cross-Domain Object Detection Algorithm for Complex End-to-End Scene Understanding

CHEN Aoran, HUANG Hai, ZHU Yueyan, XUE Junsheng   

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications
  • Received:2023-12-28 Revised:2024-02-06 Online:2024-08-28 Published:2024-08-26
  • Contact: Hai HUANG E-mail:huanghai@bupt.edu.cn

Abstract: Conventional deep learning training approaches often assume a similarity between the deployment scenario and the visual domain features present in the training data. However, this assumption might not hold true in complex end-to-end scenarios, making it difficult to meet the demands of intelligent detection services in open environments. In response, an object detection algorithm based on artificial intelligence closed-loop ensemble theory with cross-domain capabilities has been introduced. Within the detection framework, construct a backbone network and bottleneck layer network with multi-scale convolutional layers. A visual domain discriminator featuring long-range dependency attention works as a secondary detection head to refine the results. Moreover, a background focusing module, based on spatial reconstruction attention units, is able to enhance learning focused on pseudo-background representations, thereby improving the accuracy of cross-domain object detection. Experimental results show that, compared to two-stage algorithms, the proposed algorithm yields an average precision increase 6.9% , and surpasses single-stage algorithms by 9.0% in complex end-to-end scenarios.

Key words:  holistic artificial intelligence ,  computer vision ,   neural network ,  object detection

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