Efficient class-agnostic obstacle detection for UAV-assisted waterway inspection systems

@Article {
title = {Efficient class-agnostic obstacle detection for UAV-assisted waterway inspection systems},
journal = {IET Computer Vision},
pages = {1087-1096},
volume = {18},
keywds = {
Computer vision; object detection; object tracking; real-time systems
}
abstract = {

Ensuring the safety of water airport runways is essential for the correct operation of seaplane flights. Among other tasks, airport operators must identify and remove various objects that may have drifted into the runway area. In this paper, the authors propose a complete and embedded-friendly waterway obstacle detection pipeline that runs on a camera-equipped drone. This system uses a class-agnostic version of the YOLOv7 detector, which is capable of detecting objects regardless of its class. Additionally, through the usage of the GPS data of the drone and camera parameters, the location of the objects are pinpointed with 0.58 m Distance Root Mean Square. In our own annotated dataset, the system is capable of generating alerts for detected objects with a recall of 0.833 and a precision of 1.

}
doi = {10.1049/cvi2.12319},
date = {2024-12-01},
}