Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles.

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.