Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection

• URL: http://arxiv.org/abs/2309.11069v1
• Date: Wed, 20 Sep 2023 05:25:12 GMT
• Title: Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection
• Authors: Son The Nguyen, Theja Tulabandhula, Duy Nguyen
• Abstract summary: Dynamic Tiling is a model-agnostic, adaptive, and scalable approach for small object detection. Our method effectively resolves fragmented objects, improves detection accuracy, and minimizes computational overhead. Overall, Dynamic Tiling outperforms existing model-agnostic uniform cropping methods.
• Score: 3.8332251841430423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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