DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection
<|reference_start|>DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection: 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).<|reference_end|>
@article{eryuksel2024droboost:,
    title={DroBoost: An Intelligent Score and Model Boosting Method for Drone
  Detection},
    author={Ogulcan Eryuksel, Kamil Anil Ozfuttu, Fatih Cagatay Akyon, Kadir
  Sahin, Efe Buyukborekci, Devrim Cavusoglu, Sinan Altinuc},
    journal={arXiv preprint arXiv:2407.00830},
    year={2024},
    doi={10.1007/978-3-031-13324-4_34},
    archivePrefix={arXiv},
    eprint={2407.00830},
    primaryClass={cs.CV}
}