Effective pest monitoring is crucial for high-quality vegetable cultivation. While deep learning-based pest detection methods excelling at detecting large- and medium-sized pests, they face challenges with small-sized pests. To address the problem, a you only look once (YOLO)-based small-sized vegetable pest detection method was presented, named YOLO SVP. To emphasize crucial small-sized pest features and improve feature fusion, a dynamic weighting attention (DWA) mechanism was constructed and integrated into the C3k2 block of YOLO 11, yielding a new block denoted C3k2 DWA. Additionally, to preserve critical spatial information during downsampling and reduce the loss of small pest features, a space-to-depth downsampling (SPD Down) block was proposed. Besides, to alleviate the severe weakness of bounding box regression in the case of small pests, the normalized Wasserstein distance (NWD) loss function was introduced. Experimental simulation on a self-built vegetable pest dataset demonstrated the effectiveness of the proposed YOLO SVP, which achieved 85.7% F1 score, 89.3% mAP??, and 54.9% mAP??:??, outperforming YOLO 11 by 4.5, 3.8, and 4.3 percentages points, respectively. For the Frankliniella occidentalis (small-sized pest), the detection performance improved the F1 score, mAP??, and mAP??:?? by 6.3, 8.5, and 5.0 percentages points, respectively. This research provided a paradigm for adapting deep learning architectures to challenge small-sized object detection tasks in precision agriculture, which would provide important support for the effective monitoring of vegetable pests.