Enhancing Tiny Object Detection without Fine Tuning: Dynamic Adaptive Guided Object Inference Slicing Framework with Latest YOLO Models and RT-DETR Transformer

Tiny Object Detection (TOD) in high-resolution imagery presents persistent challenges in computer vision, including low resolution, occlusion, and cluttered backgrounds. This paper introduces the Dynamic Adaptive Guided Object Inference Slicing (GOIS) framework, a novel two-stage adaptive slicing approach that dynamically reallocates computational resources to Regions of Interest (ROIs). This methodology significantly enhances detection precision and efficiency, achieving 3–4× improvements in Average Precision (AP) and Average Recall (AR) metrics for small objects. Additionally, the framework demonstrates substantial gains of 50–60% across other metrics, ensuring robust performance across various object scales. While slight declines in large-object detection were noted in specific scenarios, GOIS consistently excels in detecting small and medium-sized objects, effectively addressing critical challenges inherent to TOD. The GOIS framework integrates adaptive slicing, multi-scale representation, and context-aware modeling, surpassing the limitations of static slicing methods by mitigating boundary artifacts and optimizing computational efficiency. Its architecture-agnostic design allows seamless integration with diverse state-of-the-art detection models, including YOLO11, RT-DETR-L, and YOLOv8n, without requiring extensive retraining. Rigorous validation on the VisDrone2019-DET dataset, supplemented by evaluations on low-resolution images, video streams, and live camera feeds, highlights GOIS’s transformative potential. These findings establish its applicability to critical domains such as UAV-based surveillance, autonomous navigation, and precision diagnostics. The code and results are publicly available at https: // github. com/ MMUZAMMUL/ GOIS with a live demonstration accessible at https: // youtu. be/ T5t5eb_ w0S4 .