DAHI: a fast and efficient density aided hyper inference technique for large scene object detection

Abstract

Detecting small objects in large-scale scenes remains a fundamental challenge in object detection, primarily due to scale variation, occlusion, and limited resolution. In order to contribute in this research topic, we propose Density Aided Hyper Inference (DAHI), a lightweight and detector-agnostic framework that enhances detection performance through a structured, three-stage inference process. DAHI combines: (i) Region Density Estimation (RDE), which identifies areas likely to contain overlooked objects; (ii) Density-Aided Crop Selection (DACS), which efficiently selects high-density, low-overlap regions for re-inference; and (iii) Crop Margin Aware NonMaximum Suppression (CMA-NMS), which merges detections from full-image and region-based inferences while mitigating boundary-related errors. DAHI requires no retraining and integrates seamlessly with standard object detectors. Experiments on several aerial and driving detection benchmarks demonstrate improved detection quality and runtime efficiency compared to existing multi-inference approaches, while introducing reduced computational overhead. These results support the use of DAHI as an effective and practical enhancement for small object detection in complex visual scenes.