RINKA: efficient artificial neural network model for drone-based object detection
With the rapid development of unmanned aerial vehicle (UAV) technology, drone-based aerial imaging and remote sensing have gained importance as effective tools in civil and military applications. Despite their many advantages, object detection in images acquired from UAVs remains a challenge due to scale variability, complex backgrounds, low object resolution, and varying environmental conditions. In response to these difficulties, this paper proposes a new object detection model, RINKA (Repeated efficient layer aggregation network, INvolution, Kolmogorov-Arnold), which combines the Involution mechanism with an architecture based on the Kolmogorov-Arnold network. This model was designed with adaptability to local image features and high computational efficiency in mind. As part of the experiments, the RINKA model was compared with modern detection architectures from the YOLO family (YOLOv8. YOLOv9, YOLOv10, YOLOv11). The results showed that RINKA achieved the highest evaluation metrics, confirming its effectiveness in diverse conditions.