关键词: YOLOv11, 参数高效微调, 目标检测, 低秩自适应, 深度学习

Abstract: In view of the limitations of deep learning algorithms' generalization and robustness, as well as the high computational cost of full parameter fine-tuning (FPFT) in object detection tasks in complex scenarios, an efficient fine-tuning algorithm for low-rank adaptive parameters based on YOLOv11 (You Only Look Once version 11) was proposed. Firstly, a Low-rank Adaptation (LrAn) module was embedded into the backbone network of YOLOv11. Secondly, three low-rank decomposition algorithms, which include Low-Rank Adaptation (LoRA), weight-Decomposed low-Rank Adaptation (DoRA) and Principal Singular values and Singular vectors Adaptation (PiSSA) were combined. Efficient parameter updates were achieved through weight decomposition and dynamic adjustment mechanisms. Finally, during the training process, most of the pre-trained weights of the YOLOv11 network were kept frozen, and only the low rank matrices generated by the three low rank decomposition algorithms in the LrAn module were trained, reducing the trainable parameter size to 1.56% of the original model. Experiments conducted on the COCO (Common Objects in Context) dataset demonstrate that the proposed algorithm improves the precision, recall and average precision by 4.18, 7.11 and 7.85 percentage points respectively compared with the baseline YOLOv11 algorithm. It can be seen that the proposed algorithm provides an effective technical path for lightweight and efficient fine-tuning of large-scale detection algorithms in resource constrained scenarios.

Key words: YOLOv11, Efficient parameter fine-tuning, Object detection, Low-rank adaptive, Deep learning