@inproceedings{268bc0502b3c4fbfaf1ad3ea712d56ac,
title = "YOLOv8-lite: An Interpretable Lightweight Object Detector for Real-Time UAV Detection",
abstract = "UAV detection is an important problem in sensitive areas involving security and privacy. This paper proposes an interpretable lightweight model designed explicitly for the real-time detection of UAVs, called YOLOv8-lite. By employing a high-speed YOLOv8 model and Depthwise convolution, the model performs better than the original YOLOv8 with fewer parameters in the Det-fly dataset. The proposed YOLOv8-lite achieves impressive results with 0.98 AP50 and 0.68 AP0.5:0.95 on the test set, using only 2 million parameters. Meanwhile, YOLOv8-lite shows good results in solving the challenges of detecting UAVs against various environmental backgrounds. In addition, interpretability methods are applied to illustrate the factors contributing to the effectiveness and generalization capability of the model. The code for the model is available: https://github.com/hawkinglai/uav-det.",
keywords = "Depthwise convolution, Interpretable machine learning, Object detection, UAV detection, YOLOv8",
author = "Hawking Lai and Bowie Liu and Kan, \{Ho Yin\} and Lam, \{Chan Tong\} and Im, \{Sio Kei\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Computer and Communications, ICCC 2023 ; Conference date: 08-12-2023 Through 11-12-2023",
year = "2023",
doi = "10.1109/ICCC59590.2023.10507293",
language = "English",
series = "2023 9th International Conference on Computer and Communications, ICCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1707--1713",
booktitle = "2023 9th International Conference on Computer and Communications, ICCC 2023",
address = "United States",
}