# Maritime Small Object Detection from UAVs using Deep Learning with Altitude-Aware Dynamic Tiling Source: [Maritime Small Object Detection from UAVs using Deep Learning with Altitude-Aware Dynamic Tiling]() Collection index: [llms.txt]() Data updated: 2025-11-27T05:02:30.231Z Authors: Sakib Ahmed, Oscar Pizarro Published: 2025-11-24T21:45:01.000Z ## Abstract Unmanned Aerial Vehicles (UAVs) are crucial in Search and Rescue (SAR) missions due to their ability to monitor vast maritime areas. However, small objects often remain difficult to detect from high altitudes due to low object-to-background pixel ratios. We propose an altitude-aware dynamic tiling method that scales and adaptively subdivides the image into tiles for enhanced small object detection. By integrating altitude-dependent scaling with an adaptive tiling factor, we reduce unnecessary computation while maintaining detection performance. Tested on the SeaDronesSee dataset \[1\] with YOLOv5 \[2\] and Slicing Aided Hyper Inference (SAHI) framework \[3\], our approach improves Mean Average Precision (mAP) for small objects by 38% compared to a baseline and achieves more than double the inference speed compared to static tiling. This approach enables more efficient and accurate UAV-based SAR operations under diverse conditions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()