Modification Of Yolov11 Nano And Small Architecture For Improved Accuracy In Motorcycle Riders Face Recognition Based On Eye - Ardiansyah, Randy; Wirarama WW, I Gde Putu; Husodo, Ario Yudo (Jurnal Teknik Informatika (Jutif), 2025)
Face recognition still faces challenges in identifying faces covered by masks and helmets with open visors, such as those commonly used by motorcyclists, especially when entering parking areas. To improve the accuracy of face recognition in these conditions, this study proposes nano and small versions of the YOLOv11 modification, which is an internal version. Modifications are made to the neck section and the DySample module is added in place of the UpSample module to improve the model's capabilities. Experiments were conducted using a self-generated dataset consisting of 50 classes. The results show that the modified nano version achieves 99.3% accuracy at the same mAP50 as YOLOv11n and YOLOv12n. At mAP50-95, it shows a 1.6% accuracy improvement compared to YOLOv11n and YOLOv12n with 75% accuracy. Meanwhile, the modified small version achieved an accuracy improvement of 1.3% and 1.2% compared to YOLOv11n and YOLOv12n, respectively, reaching 76.1% on mAP50-95, although the accuracy on mAP50 remained the same as YOLOv11n and 0.1% superior to YOLOv12n. However, recall and precision did not show significant improvement in both as well as the increase in model parameters. However, the model is still in the nano and small versions. Therefore, the model can be implemented on edge devices. This research is important for the field of computer vision, especially in the context of face recognition. The contribution of this research is the improvement of the accuracy of the mAP50-95 metric in eye-based face recognition, which is relevant for intelligent security systems with limited resources.