An optimized YOLO NAS based framework for realtime object detection

Abstract An enhanced version of the YOLO-NAS object detection network model has been presented in this paper, and MISH activation and Artificial Bee Colony (ABC) optimization algorithms are integrated. MISH functional adds non-monotonic behavior, which at the same time enhances the feature representation and complements the gradient flow. ABC optimization that assists in the optimization of the hyperparameters to a ground truth and resistance to the models. The given model is tested on the dataset that is introduced by the researchers themselves, and it shows better results compared to baselines based on the YOLO-NAS variants in precision, recall, and mean average precision (mAP) measures. Experiments prove the fact that a combination of a biologically inspired optimizer and a contemporary activation function helps to make training more stable and predictions more accurate. The results show that the proposed fine-tuned YOLO-NAS model outperformed the other tested models, that is, YOLOv6, YOLOv7, and YOLOv8, in the three metrics of accuracy, recall, precision, F1 score, and mAP at 0.50, 0.75, and 0.95 on the test dataset. The proposed model achieved an accuracy of 98% while recognizing real-time objects.