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Updated: Jul 16, 2026

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HRL-Det: Hierarchical Reinforcement Learning for Sequential Object Detection in Aerial Imagery.

Meng Li1, Yaowen Hu2

  • 1College of Geography and Environment, Xianyang Normal University, Xianyang 712000, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
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This study introduces HRL-Det, a novel hierarchical reinforcement learning (RL) framework for efficient object detection in drone imagery. It significantly improves accuracy and reduces computational cost by using continuous-time state evolution and guided reward shaping.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Object detection in unmanned aerial vehicle (UAV) imagery faces challenges like scale variation, dense packing, and high computational costs.
  • Existing reinforcement learning (RL)-based sequential detectors struggle with discrete states, sparse rewards, and policy collapse.

Purpose of the Study:

  • To propose HRL-Det, a hierarchical reinforcement learning framework to overcome limitations in RL-based object detection for UAVs.
  • To enhance temporal reasoning and enable efficient active search processes for object localization.

Main Methods:

  • Developed a Neural ODE-driven Continuous-Time Bellman State Evolution module for fine-grained temporal reasoning using stochastic differential equations.
  • Implemented a Lyapunov-Guided Entropy-Regularized Reward Shaping mechanism for convergence-promoting dense rewards and exploration diversity.
Keywords:
Lyapunov stabilitydeep Q-networkhierarchical reinforcement learningneural ordinary differential equationobject detectionreinforcement learningreward shapingunmanned aerial vehicle

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  • Utilized memory-efficient adjoint-based backpropagation for state dynamics modeling.

    • Main Results:

    • HRL-Det achieved mAP@0.5 scores of 0.412 on VisDrone2019, 0.812 on DroneVehicle, and 0.735 on MS COCO 2017.
    • Outperformed existing RL-based detectors and showed competitive accuracy against non-RL detectors.
    • Required only 17.3 M parameters and an average of 6.3 search steps per object, demonstrating significant efficiency gains.

    Conclusions:

    • HRL-Det offers a robust and efficient solution for object detection in challenging UAV scenarios.
    • The framework effectively addresses scale variation, dense packing, and computational cost issues.
    • The proposed innovations in continuous-time state evolution and reward shaping contribute to improved performance and efficiency.