Bio-Inspired Object Detection and Tracking in Aerial Images: Harnessing Northern Goshawk Optimization

This study presents a novel approach for object detection and tracking in aerial images using a multi-scale Northern Goshawk Pyramid Generative Adversarial Network (NGPGAN). The research evaluates different algorithms and features to identify people, trees, cars, and buildings in real-world drone videos, addressing challenges in pinpointing specific objects among multiple entities. Object detection and tracking are crucial tasks in various industries, prompting increased exploration of machine learning, particularly deep learning techniques. The proposed NGPGAN model integrates object detection and tracking stages, leveraging the Kalman filter with Northern Goshawk Optimization (NGO) for tracking and employing NGPGAN for detection. To enhance training stability, Northern Goshawk Optimization is utilized to optimize the generator's cost and loss functions, mitigating issues like non-convergence and mode collapse. The study evaluates the proposed architecture's performance using aerial drone data, focusing on efficiency and accuracy compared to existing methods.