mlsanityData to 14 Oct 2026

OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images

arXiv 2304.11805 ↗First posted 24 Apr 2023cs.CVcited by 8

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Abstract (arXiv)

Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation characteristic. And we found that extracting or localizing occlusion between objects is beneficial for the detector to address this challenge. According to this finding, the occlusion localization task is introduced, which together with the object detection task constitutes our occlusion-guided multi-task network (OGMN). The OGMN contains the localization of occlusion and two occlusion-guided multi-task interactions. In detail, an occlusion estimation module (OEM) is proposed to precisely localize occlusion. Then the OGMN utilizes the occlusion localization results to implement occlusion-guided detection with two multi-task interactions. One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head. Another interaction for guide is designed in the detection process according to local aggregation characteristic, and a two-phase progressive refinement process (TPP) is proposed to optimize the detection process. Extensive experiments demonstrate the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%. And our OGMN provides a new insight for accurate occlusion localization and achieves competitive detection performance.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 33 of its 61 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

33 of 61 references.

References · 33

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21 more
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Built on by

Cited by year

Citing papers per year: 2024: 6; 2025: 2.

8 papers.

  1. 2024RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learningcited by 15
  2. 2024HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenescited by 15
  3. 2024SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensingcited by 3
  4. 2024SCLNet: A Scale-Robust Complementary Learning Network for Object Detection in UAV Imagescited by 1
  5. 2025Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Imagescited by 1
  6. 2025Teaching in adverse scenes: a statistically feedback-driven threshold and mask adjustment teacher-student framework for object detection in UAV images under adverse scenescited by 1
  7. 2024Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenescited by 0
  8. 2024UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretationcited by 0