Overlapping Box Suppression and Merging Algorithms for Window-Based Object Detection

In this manuscript, we extend the Overlapping Box Suppression (OBS) algorithm, a novel approach designed to enhance window-based object detection systems by reducing false-positive detections. While window-based methods are commonly used for small object detection, they often face challenges due to partially visible objects and intersecting detection windows. To address this, the proposed OBS algorithm uses the detection window coordinates to effectively filter out redundant partial detections, improving detection quality. Additionally, we introduce a novel Overlapping Box Merging (OBM) algorithm, which further refines detection results by combining partial detections into a single, more accurate detection. Together, OBS and OBM offer a robust solution for handling overlapping and fragmented detections. We evaluate this combined global filtering block on sequences from the SeaDronesSee dataset, demonstrating superior performance across multiple object detection metrics compared to traditional NMS-based filtering methods.