Detecting aerial objects: drones, birds, and helicopters

Standard object detectors are trained on a wide array of commonplace objects and work out-of-the-box for numerous every-day applications. Training data for these detectors tends to have objects of interest that appear prominently in the scene making them easy to identify. Unfortunately, objects seen by camera sensors in the real-world scenarios typically do not always appear large, in-focus, or towards the center of an image. In the face of these problems, the performance of many detectors lags behind the necessary thresholds for their successful implementation in uncontrolled environments. Specialized applications necessitate additional training data to be reliable in-situ, especially when small objects are likely to appear in the scene. In this paper, we present an object detection dataset consisting of videos that depict helicopter exercises recorded in an unconstrained, maritime environment. Special consideration was taken to emphasize small instances of helicopters relative to the field-of-view and therefore provides a more even ratio of small-, medium-, and large-sized object appearances for training more robust detectors in this specific domain. We use the COCO evaluation metric to benchmark multiple detectors on our data as well as the WOSDETC (Drone Vs. Bird) dataset; and, we compare a variety of augmentation techniques to improve detection accuracy and precision in this setting. These comparisons yield important lessons learned as we adapt standard object detectors to process data with non-iconic views from field-specific applications.