Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6): 263-269.doi: 10.11896/jsjkx.250700103

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Object Detection Method Based on Dynamic Feature Fusion

LIU Jikang1, HUANG Lei1, ZHANG Ke1, NIE Jie1, WEI Zhiqiang1,2   

  1. 1 Faculty of Information Science and Engineering,Ocean University of China,Qingdao,Shandong 266000,China
    2 College of Computer Science & Technology,Qingdao University,Qingdao,Shandong 266001,China
  • Received:2025-07-16 Revised:2025-10-20 Online:2026-06-15 Published:2026-06-09
  • About author:LIU Jikang,born in 1998,postgraduate,is a member of CCF(No.A01785G).His main research interests include computer vision and object detection.
    HUANG Lei,born in 1983,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.17587M).His main research interests include multimedia content analysis and retrieval,computer vision,machine learning, and marine big data analysis.
  • Supported by:
    National Natural Science Foundation of China(62472390) and Shandong Provincial Natural Science Foundation(ZR2023MF033).

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Abstract: Object detection is a foundational task in computer vision,with wide-ranging applications in autonomous driving,intelligent transportation,and medical diagnosis.DETR(DEtection TRansformer) pioneers an end-to-end Transformer-based detection framework that eliminates hand-crafted components.However,its decoder's strong inter-layer dependencies give rise to cascading negative optimization during decoding,impeding both training efficiency and accuracy.To address this limitation,this paper proposes DFF DETR(Dynamic Feature Fusion DETR),which dynamically fuses cross-layer features to enable effective propagation of object queries across decoder layers and reduce overreliance on preceding outputs.Additionally,it introduces an inter-layer supervisory signal during backpropagation to refine intermediate query representations and correct suboptimal early outputs.Extensive experiments on various DETR-based detectors demonstrate that integrating DFF DETR yields an average mAP improvement of approximately 1.0%,with particularly pronounced gains in small-object detection.

Key words: Object detection, Feature fusion, Supervision signal, DEtection TRansformer, Object query

CLC Number: 

  • TP391.4
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