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A weakly-supervised oriented object detector : Knowledge-based dropblock and unified regression network.

Lijuan Duan1, Zichen Zhang2, Zhaoying Liu3

  • 1College of Computer Science, Beijing University of Technology, Beijing, 100124, China; Chongqing Research Institute, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Trusted Computing, Beijing University of Technology, Beijing, 100124, China.

Neural Networks : the Official Journal of the International Neural Network Society
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Summary
This summary is machine-generated.

This study introduces KDUNet, a novel weakly-supervised object detector for remote sensing images. KDUNet improves location accuracy by emphasizing entire objects and unifying rotated and horizontal bounding boxes, outperforming fully-supervised methods.

Keywords:
Attention guided dropblockOriented object detectionRemote sensing imagesUnified regression mechanism

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Machine Learning

Background:

  • Object detection in remote sensing images (RSIs) commonly uses oriented bounding boxes (RBoxes), which are more labor-intensive than horizontal boxes (HBoxes).
  • Existing weakly-supervised detectors for HBoxes often focus on discriminative object parts, reducing location accuracy.
  • Spatial transformations in weakly-supervised methods create ambiguity between RBoxes and HBoxes, hindering the detection of closely situated objects.

Purpose of the Study:

  • To propose a novel weakly-supervised object detector, KDUNet, that learns high-quality features and addresses the disparity between RBoxes and HBoxes.
  • To enhance object localization accuracy by emphasizing the entire object rather than just discriminative parts.
  • To develop a unified regression approach for both RBoxes and HBoxes to mitigate detection ambiguity.

Main Methods:

  • KDUNet utilizes long-distance background information and diverse channel inputs to obscure discriminative object parts, promoting focus on the entire object.
  • A novel bounding box distance measure is introduced, unifying RBoxes and HBoxes via a circumscribed rectangle and transformation angle for Gaussian distance assessment.
  • The network is trained to learn high-quality feature information and compensate for the inherent ambiguity between different bounding box types.

Main Results:

  • KDUNet demonstrates the capability to learn high-quality feature information and effectively reduce ambiguity in object detection.
  • On the DIOR dataset, KDUNet achieved a mean Average Precision (mAP) of 57.8%, surpassing six fully-supervised networks.
  • On the HRSC dataset, KDUNet achieved a mean Average Precision (mAP) of 90.1%, outperforming six fully-supervised networks.

Conclusions:

  • KDUNet offers a significant advancement in weakly-supervised object detection for remote sensing images.
  • The proposed methods effectively address the limitations of existing approaches, leading to improved accuracy and robustness.
  • KDUNet's performance validates its potential for practical applications in remote sensing image analysis.