AYOLO - Development of a Real-Time Object Detection Model for the Detection of Secretly Cultivated Plants

AYOLO differentiates itself with a new fusion architecture proposal that leverages the strengths of unsupervised learning and integrates the Vision Transformer approach, taking the YOLO series models as a reference. This innovation enables the model to effectively use rich, unlabeled data, providing a new example in pre-training methodology for YOLO architectures. On the benchmark COCO val2017 dataset, AYOLO demonstrates its superiority by achieving an impressive 38.7 % AP while maintaining an outstanding rendering speed of 239 FPS (Frame Per Second) on the Tesla K80 GPU. This performance outperforms the previous state-of-the-art YOLO v6-3.0 by a significant margin of +2.2 % AP while maintaining comparable FPS. AYOLO is presented as a demonstration of the potential of integrating complex information fusion techniques with supervised pretraining in improving the precision and speed of object detection models.