Object detection on aerial imagery using CenterNet
This work addresses object detection challenges in aerial imagery for applications like urban planning, but it is incremental as it applies an existing method to a new dataset.
The paper tackled object detection in aerial imagery by evaluating CenterNet on the VisDrone2019 dataset, achieving results with different backbone networks and resolutions, though no specific performance numbers were provided.
Detection and classification of objects in aerial imagery have several applications like urban planning, crop surveillance, and traffic surveillance. However, due to the lower resolution of the objects and the effect of noise in aerial images, extracting distinguishing features for the objects is a challenge. We evaluate CenterNet, a state of the art method for real-time 2D object detection, on the VisDrone2019 dataset. We evaluate the performance of the model with different backbone networks in conjunction with varying resolutions during training and testing.