论文标题
penet：使用空中图像中点估计的对象检测
PENet: Object Detection using Points Estimation in Aerial Images
论文作者
论文摘要
在关键任务（例如交通监视，智能城市和灾难援助）等任务中越来越多地采用了空中图像。但是，从空中图像中识别物体面临以下挑战：1）感兴趣的对象通常太小，相对于图像而言太密集； 2）感兴趣的对象通常具有不同的相对大小； 3）每个类别中的对象数量不平衡。在这项工作中提出了一种新颖的网络结构，即估计的网络（PENET），以应对这些挑战。 Penet使用面膜重新采样模块（MRM）来增强不平衡数据集，无锚固剂的检测器（CPEN），以有效预测小物体簇的中心点，以及无锚固剂的探测器FPEN，以定位小物体的精确位置。在CPEN中实施了自适应合并算法非最大合并（NMM），以解决检测密集的小物体的问题，并在FPEN中定义了层次损失，以进一步提高分类精度。我们在空中数据集Visdrone和UAVDT上进行的广泛实验表明，PENET比现有的最新方法获得了更高的精度结果。我们的最佳模型可在Vistrone上提高8.7％，而UAVDT的型号为20.3％。
Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance. However, identifying objects from aerial images faces the following challenges: 1) objects of interests are often too small and too dense relative to the images; 2) objects of interests are often in different relative sizes; and 3) the number of objects in each category is imbalanced. A novel network structure, Points Estimated Network (PENet), is proposed in this work to answer these challenges. PENet uses a Mask Resampling Module (MRM) to augment the imbalanced datasets, a coarse anchor-free detector (CPEN) to effectively predict the center points of the small object clusters, and a fine anchor-free detector FPEN to locate the precise positions of the small objects. An adaptive merge algorithm Non-maximum Merge (NMM) is implemented in CPEN to address the issue of detecting dense small objects, and a hierarchical loss is defined in FPEN to further improve the classification accuracy. Our extensive experiments on aerial datasets visDrone and UAVDT showed that PENet achieved higher precision results than existing state-of-the-art approaches. Our best model achieved 8.7% improvement on visDrone and 20.3% on UAVDT.
