S-Feature Pyramid Network and Attention Model for Drone Detection
Pengcheng Dong, Chuntao Wang, Zhenyong Lu, Kai Zhang, Wenbo Wan, Jiande Sun
Abstract
The issue of aviation safety has always received a great of attention and focus, and birds are also an important issue in aviation safety. Nowadays, drones have emerged and share the same airspace with birds at low altitudes. The problems associated with drones should also be taken into account. For example, small drones can be misused for illegal activities and the threat from them is on the rise. Driven by this situation, we used data provided by the ICASSP Drone-vs-Bird detection Grand Challenge for drone detection and used the method of adding shallow feature pyramid network and attention model on SSD [1] (SFA-SSD) to solve the problem of drone detection in competition. Out of 30 test videos, our method was able to detect drones in 11 videos, with 8 videos scoring above 0.1 and only 3 videos scoring above 0.7.
BibTeX
@inproceedings{icassp2023_sfeaturepyramidn,
title = {S-Feature Pyramid Network and Attention Model for Drone Detection},
author = {Pengcheng Dong and Chuntao Wang and Zhenyong Lu and Kai Zhang and Wenbo Wan and Jiande Sun},
booktitle = {ICASSP 2023},
year = {2023}
}