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， a low-rank adaptive Parameter-Efficient Fine-Tuning （PEFT） algorithm based on YOLOv11 （You Only Look Once version 11） was proposed. Firstly， a Low-Rank Adaptation （LoRA） module was embedded into the backbone and neck networks of YOLOv11. Secondly， three low-rank decomposition algorithms， including LoRA， weight-Decomposed low-Rank Adaptation （DoRA） and Principal Singular values and Singular vectors Adaptation （PiSSA） were combined， and 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 LoRA module were trained， thereby reducing the trainable parameter size to 1.56% of the original algorithm. Experimental results on the COCO （Common Objects in COntext） dataset demonstrate that the proposed algorithm improves the precision， recall and mean Average Precision （mAP） at IoU （Intersection over Union） threshold of 0.5 by 4.18， 7.11 and 7.85 percentage points， respectively， compared with the baseline algorithm YOLOv11. 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.