The article dissects EFSI‑DETR, a UAV small‑object detector that combines simulated frequency processing with dynamic semantic enhancement to overcome pixel scarcity, static fusion, and ignored frequency cues, delivering 188 FPS and a 5.8% APₛ gain on VisDrone while remaining lightweight.
This article analyzes why multimodal large language models often fail to detect small objects, identifies three core bottlenecks, and presents a four‑tiered optimization roadmap—from zero‑cost inference tricks to data augmentation, model fine‑tuning, and engineering safeguards—backed by three real‑world case studies and actionable guidelines.
The study enhances UI2CODE’s ability to detect tiny UI components by augmenting training data with copied small objects, upgrading the detector from Faster RCNN to FPN and Cascade FPN, and refining box positions with smoothing and projection, achieving superior small‑object mAP/mAR and enabling broader UI parsing applications.
