  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.
