Foreign object debris detection in lane images using deep learning methodology.

ABSTRACT:

Background

Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install.

Methods

This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. Th

SUBMITTER: S P 

PROVIDER: S-EPMC11784716 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature