基于高度自适应分块，提升无人机海面小目标检测效率与精度
Maritime Small Object Detection from UAVs using Deep Learning with Altitude-Aware Dynamic Tiling
- 根据飞行高度动态调整图像分块大小和数量
- 小目标mAP提升38%，推理速度翻倍
- 适合复杂海况下无人机搜救任务
无人机在海上搜救中至关重要，但高空拍摄时小目标像素占比低，难以检测。本文提出一种高度感知的动态分块方法，根据飞行高度自适应缩放和分割图像，减少冗余计算同时保持检测性能。在SeaDronesSee数据集上，结合YOLOv5与SAHI框架测试，相比基线方法小目标mAP提升38%，推理速度超过静态分块两倍。该方法提升了不同条件下无人机海上搜救的效率与准确性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。