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<p>Unmanned aerial vehicles (UAVs) are increasingly employed in applications such as surveillance, disaster management, traffic monitoring, and precision agriculture. However, real-time object detection platforms remain a challenging task due to limited onboard computational resources, stringent latency requirements, dynamic network conditions, and complex environmental variations including illumination changes, occlusion, fast motion, and low-resolution targets. Existing UAV-based detection systems often rely on static edge or cloud processing strategies, which limits their adaptability and degrades performance in dynamically changing operational scenarios. To address these challenges, this paper proposes an Adaptive Reinforced Detection and Tracking Network (ARDT-Net), which integrates a lightweight YOLOv9-Lite detector, Soft Actor-Critic (SAC)-based adaptive offloading, and reinforcement learning object tracking (RLOT) for robust edge–cloud UAV in complex environments. The framework employs YOLOv9-Lite as a lightweight edge-side detector to enable low-latency inference on resource-constrained UAV platforms. To efficiently balance computational load and detection accuracy, a SAC-based reinforcement learning mechanism is introduced to dynamically control task offloading decisions between the UAV edge and the cloud, considering workload complexity, network conditions, and available system resources. Furthermore, a RLOT module is integrated at the cloud level to refine detection results and maintain temporal consistency under challenging conditions. The proposed framework also incorporates efficient feature extraction and multi-scale feature aggregation to enhance robustness while preserving computational efficiency. Extensive experiments conducted on the UAVDT, VISDRONE, AU-AIR, and DRONEVEHICLE datasets, which includes diverse lighting conditions, viewpoints, and low-resolution targets, demonstrate the effectiveness of the proposed approach. The ARDT-Net framework consistently achieves superior precision and success rates compared to State-Of-The-Art (SOTA) UAV object detection methods. Ablation studies further confirm that the SAC-driven adaptive offloading strategy and the RLOT module achieve robust, low-latency, and accurate UAV object detection in real-world scenarios.</p>

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Real-time UAV object detection and task offloading: powered through edge YOlOv9-Lite and cloud reinforcement transformer learning in various scenarios

  • 期刊论文

  • 发表日期：

    2026年7月25日

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  • M. Saravana Karthikeyan,
  • C. Arun,
  • T. Gobinath,
  • R. Megiba Jasmine

摘要

Unmanned aerial vehicles (UAVs) are increasingly employed in applications such as surveillance, disaster management, traffic monitoring, and precision agriculture. However, real-time object detection platforms remain a challenging task due to limited onboard computational resources, stringent latency requirements, dynamic network conditions, and complex environmental variations including illumination changes, occlusion, fast motion, and low-resolution targets. Existing UAV-based detection systems often rely on static edge or cloud processing strategies, which limits their adaptability and degrades performance in dynamically changing operational scenarios. To address these challenges, this paper proposes an Adaptive Reinforced Detection and Tracking Network (ARDT-Net), which integrates a lightweight YOLOv9-Lite detector, Soft Actor-Critic (SAC)-based adaptive offloading, and reinforcement learning object tracking (RLOT) for robust edge–cloud UAV in complex environments. The framework employs YOLOv9-Lite as a lightweight edge-side detector to enable low-latency inference on resource-constrained UAV platforms. To efficiently balance computational load and detection accuracy, a SAC-based reinforcement learning mechanism is introduced to dynamically control task offloading decisions between the UAV edge and the cloud, considering workload complexity, network conditions, and available system resources. Furthermore, a RLOT module is integrated at the cloud level to refine detection results and maintain temporal consistency under challenging conditions. The proposed framework also incorporates efficient feature extraction and multi-scale feature aggregation to enhance robustness while preserving computational efficiency. Extensive experiments conducted on the UAVDT, VISDRONE, AU-AIR, and DRONEVEHICLE datasets, which includes diverse lighting conditions, viewpoints, and low-resolution targets, demonstrate the effectiveness of the proposed approach. The ARDT-Net framework consistently achieves superior precision and success rates compared to State-Of-The-Art (SOTA) UAV object detection methods. Ablation studies further confirm that the SAC-driven adaptive offloading strategy and the RLOT module achieve robust, low-latency, and accurate UAV object detection in real-world scenarios.