The field of object detection is moving towards more efficient and accurate models, with a focus on improving performance in challenging environments such as high-altitude drone images. Researchers are exploring new architectures and techniques, including the use of mixture-of-experts models and adaptive routing, to achieve state-of-the-art results. Notable papers in this area include YOLO-Drone, which introduces a GhostHead Network to improve object detection accuracy, and ERMoE, which proposes a sparse MoE transformer for stable routing and interpretable specialization. These innovations have the potential to advance the field of object detection and enable more effective applications in areas such as facial expression recognition and real-time emotion-aware AI.