Alrayes Fatma S, Ahmad Nazir, Alshuhail Asma, Alshammeri Menwa, Alqazzaz Ali, Alkhiri Hassan, Alqurni Jehad Saad, Said Yahia
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia.
Sci Rep. 2025 Aug 2;15(1):28248. doi: 10.1038/s41598-025-09652-1.
Unmanned aerial vehicles (UAVs) become increasingly available devices with extensive usage as environmental monitoring systems. With the benefit of higher mobility, UAVs are applied to fuel various significant uses in computer vision (CV), providing more effectiveness and accessibility than surveillance cameras with permanent camera view, angle, and scale. Nevertheless, owing to camera motion and composite environments, it is problematic to identify the UAV; conventional models frequently miss UAV detection and make false alarms. Drone-equipped cameras monitor objects at changing altitudes, leading to essential scale variants. The model increases targeted accuracy and decreases false positives using real-time data and machine learning (ML) methods. Its enormous applications range from military operations to urban planning and wildlife monitoring. Therefore, this study develops a novel long-term target detection and tracking model for unmanned aerial vehicles using a deep fusion-based convolutional transform learning (LTTDT-UAVDFCTL) model. The LTTDT-UAVDFCTL model presents a new model to improve the robustness and accuracy of target tracking and detection in scale-variant environments. At first, the presented LTTDT-UAVDFCTL technique performs image pre-processing by utilizing the median median-enhanced wiener filter (MEWF) technique to improve clarity and reduce noise. For object detection (OD), the highly accurate YOLOv8 technique is utilized, followed by feature extraction through a backbone deep fusion-based convolutional transform learning of VGG16, CapsNet, and EfficientNetB7 to capture both spatial and hierarchical features across varying scales. Moreover, the graph convolutional neural network (GCN) technique is employed for long-term target detection and tracking models. Finally, the hybrid nonlinear whale optimization algorithm with sine cosine (SCWOA) is implemented for the optimum choice of the hyperparameters involved in the GCN technique. The experimental study of the LTTDT-UAVDFCTL approach is performed under the VisDrone dataset. The performance validation of the LTTDT-UAVDFCTL approach portrayed a superior mAP value of 80.13% over existing models.
无人驾驶飞行器（UAV）作为环境监测系统，正成为越来越容易获得且用途广泛的设备。受益于更高的机动性，无人机被应用于计算机视觉（CV）中的各种重要用途，与具有固定视角、角度和比例的监控摄像头相比，具有更高的效率和可及性。然而，由于相机运动和复杂环境，识别无人机存在问题；传统模型经常错过无人机检测并产生误报。配备无人机的相机在不同高度监测物体，导致关键的比例变化。该模型使用实时数据和机器学习（ML）方法提高了目标准确性并减少了误报。其广泛的应用范围涵盖军事行动、城市规划和野生动物监测。因此，本研究开发了一种基于深度融合的卷积变换学习的新型无人机长期目标检测与跟踪模型（LTTDT-UAVDFCTL）。LTTDT-UAVDFCTL模型提出了一种新模型，以提高在比例变化环境中目标跟踪和检测的鲁棒性和准确性。首先，所提出的LTTDT-UAVDFCTL技术通过利用中值中值增强维纳滤波器（MEWF）技术进行图像预处理，以提高清晰度并减少噪声。对于目标检测（OD），采用了高精度的YOLOv8技术，随后通过基于VGG16、CapsNet和EfficientNetB7的骨干深度融合卷积变换学习进行特征提取，以跨不同比例捕获空间和层次特征。此外，图卷积神经网络（GCN）技术用于长期目标检测和跟踪模型。最后，实现了具有正弦余弦的混合非线性鲸鱼优化算法（SCWOA），以优化GCN技术中涉及的超参数选择。LTTDT-UAVDFCTL方法的实验研究是在VisDrone数据集下进行的。LTTDT-UAVDFCTL方法的性能验证表明，其平均精度均值（mAP）值比现有模型高出80.13%。
