One of the important bottlenecks in training modern object detectors is the need for labeled images where bounding box annotations have to be produced for each object present in the image. This bottleneck is further exacerbated in aerial images where the annotators have to label small objects often distributed in clusters on high-resolution images. In recent days, the mean-teacher approach trained with pseudo-labels and weak-strong augmentation consistency is gaining popularity for semi-supervised object detection. However, a direct adaptation of such semi-supervised detectors for aerial images where small clustered objects are often present, might not lead to optimal results. In this paper, we propose a density crop-guided semi-supervised detector that identifies the cluster of small objects during training and also exploits them to improve performance at inference. During training, image crops of clusters identified from labeled and unlabeled images are used to augment the training set, which in turn increases the chance of detecting small objects and creating good pseudo-labels for small objects on the unlabeled images. During inference, the detector is not only able to detect the objects of interest but also regions with a high density of small objects (density crops) so that detections from the input image and detections from image crops are combined, resulting in an overall more accurate object prediction, especially for small objects. Empirical studies on the popular benchmarks of VisDrone and DOTA datasets show the effectiveness of our density crop-guided semi-supervised detector with an average improvement of more than 2\% over the basic mean-teacher method in COCO style AP. Our code is available at: https://github.com/akhilpm/DroneSSOD.

翻译：现代目标检测器训练的重要瓶颈之一是需要标注图像，其中必须为图像中每个物体生成边界框标注。这一瓶颈在航空图像中进一步加剧，因为标注者需标注高分辨率图像中常呈簇状分布的小物体。近年来，基于伪标签和弱-强增强一致性的均值教师方法在半监督目标检测中逐渐流行。然而，直接将此类半监督检测器应用于常含小簇状物体的航空图像可能无法达到最优效果。本文提出一种密度裁剪引导的半监督检测器，其在训练过程中识别小物体簇，并利用这些簇提升推理性能。训练阶段，我们从标注与未标注图像中识别出的簇区域裁剪图像以扩充训练集，这增加了检测小物体的概率，并为未标注图像生成高质量小物体伪标签。推理阶段，检测器不仅可检测目标物体，还能识别小物体高密度区域（密度裁剪），从而结合输入图像与裁剪图像的检测结果，实现更精准的整体目标预测（尤其是小物体）。在VisDrone和DOTA数据集上的实验表明，本文提出的密度裁剪引导半监督检测器在COCO风格AP指标上相比基本均值教师方法平均提升超过2%。代码已开源：https://github.com/akhilpm/DroneSSOD。