  Drone detection is a challenging object detection task where visibility
conditions and quality of the images may be unfavorable, and detections might
become difficult due to complex backgrounds, small visible objects, and hard to
distinguish objects. Both provide high confidence for drone detections, and
eliminating false detections requires efficient algorithms and approaches. Our
previous work, which uses YOLOv5, uses both real and synthetic data and a
Kalman-based tracker to track the detections and increase their confidence
using temporal information. Our current work improves on the previous approach
by combining several improvements. We used a more diverse dataset combining
multiple sources and combined with synthetic samples chosen from a large
synthetic dataset based on the error analysis of the base model. Also, to
obtain more resilient confidence scores for objects, we introduced a
classification component that discriminates whether the object is a drone or
not. Finally, we developed a more advanced scoring algorithm for object
tracking that we use to adjust localization confidence. Furthermore, the
proposed technique won 1st Place in the Drone vs. Bird Challenge (Workshop on
Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP
2021).
