We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy. Dynamic Tiling starts with non-overlapping tiles for initial detections and utilizes dynamic overlapping rates along with a tile minimizer. This dual approach effectively resolves fragmented objects, improves detection accuracy, and minimizes computational overhead by reducing the number of forward passes through the object detection model. Adaptable to a variety of operational environments, our method negates the need for laborious recalibration. Additionally, our large-small filtering mechanism boosts the detection quality across a range of object sizes. Overall, Dynamic Tiling outperforms existing model-agnostic uniform cropping methods, setting new benchmarks for efficiency and accuracy.

翻译：我们提出动态分块（Dynamic Tiling），这是一种基于推理数据为中心理念的模型无关、自适应且可扩展的小目标检测方法。该方法从非重叠分块生成初始检测结果，并采用动态重叠率与分块最小化器协同工作。这种双重策略有效解决了目标碎片化问题，提升了检测精度，同时通过减少目标检测模型的前向传播次数来最小化计算开销。该方法可适应多种运行环境，无需繁琐的重新校准。此外，我们设计的大-小目标过滤机制能够提升跨目标尺寸范围的检测质量。总体而言，动态分块超越了现有模型无关的均匀裁剪方法，在效率与精度方面树立了新标杆。