对比4种模型在无人机航拍地雷数据集上的表现，发现YOLOF效果最佳。
Comparing Surface Landmine Object Detection Models on a New Drone Flyby Dataset
- 用无人机拍摄的俄制地雷图像训练并比较4个视觉检测模型。
- YOLOF在10米航高下达到0.89的mAP，优于其他模型约0.07。
- 开源数据集与代码，适合安全检测与军事智能研究者使用。
传统地雷探测方法耗时、危险且成本高昂。基于深度学习的无人机视频目标检测具有前景，但因近年流行的小型表面地雷（如可乐罐大小）带来挑战。现有文献缺乏对最优机器学习模型的科学评估，因多数研究集中于地面监控视频。为推动全面模型训练与相关研究，本文构建了一个包含俄制POM-2和POM-3表面地雷的自定义无人机图像数据集。在此数据集上，训练并测试了4个计算机视觉基础模型：YOLOF、DETR、Sparse-RCNN和VFNet。结果显示，所有模型表现良好，其中YOLOF以0.89的mAP领先，DETR、VFNet和Sparse-RCNN的mAP均约为0.82（航高10米AGL）。YOLOF训练仅需56分钟（单块Nvidia V100集群）。研究还公开了地雷图像与视频数据集及模型Jupyter笔记本，地址为https://github.com/UnVeilX/，以支持未来地雷检测研究。
原文摘要 · Abstract (English)
Landmine detection using traditional methods is slow, dangerous and prohibitively expensive. Using deep learning-based object detection algorithms drone videos is promising but has multiple challenges due to the small, soda-can size of recently prevalent surface landmines. The literature currently lacks scientific evaluation of optimal ML models for this problem since most object detection research focuses on analysis of ground video surveillance images. In order to help train comprehensive models and drive research for surface landmine detection, we first create a custom dataset comprising drone images of POM-2 and POM-3 Russian surface landmines. Using this dataset, we train, test and compare 4 different computer vision foundation models YOLOF, DETR, Sparse-RCNN and VFNet. Generally, all 4 detectors do well with YOLOF outperforming other models with a mAP score of 0.89 while DETR, VFNET and Sparse-RCNN mAP scores are all around 0.82 for drone images taken from 10m AGL. YOLOF is also quicker to train consuming 56min of training time on a Nvidia V100 compute cluster. Finally, this research contributes landmine image, video datasets and model Jupyter notebooks at https://github.com/UnVeilX/ to enable future research in surface landmine detection.
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