Deep Learning Based Multi-Level Classification for Aviation Safety

TL;DR

This paper introduces a CNN-based image classification system to identify bird species, flock type, and size, enhancing aviation safety by improving bird strike prediction and prevention strategies.

Contribution

It presents a novel CNN framework for real-time bird species and flock behavior classification using camera systems, addressing limitations of existing radar-based methods.

Findings

Effective bird species identification using CNNs

Accurate estimation of flock size and type

Enhanced flight path prediction for safety improvements

Abstract

Bird strikes pose a significant threat to aviation safety, often resulting in loss of life, severe aircraft damage, and substantial financial costs. Existing bird strike prevention strategies primarily rely on avian radar systems that detect and track birds in real time. A major limitation of these systems is their inability to identify bird species, an essential factor, as different species exhibit distinct flight behaviors, and altitudinal preference. To address this challenge, we propose an image-based bird classification framework using Convolutional Neural Networks (CNNs), designed to work with camera systems for autonomous visual detection. The CNN is designed to identify bird species and provide critical input to species-specific predictive models for accurate flight path prediction. In addition to species identification, we implemented dedicated CNN classifiers to estimate flock formation type and flock size. These characteristics provide valuable supplementary information for aviation safety. Specifically, flock type and size offer insights into collective flight behavior, and trajectory dispersion . Flock size directly relates to the potential impact severity, as the overall damage risk increases with the combined kinetic energy of multiple birds.