Deep Learning Based Multi-Level Classification for Aviation Safety

2026-02-01

AI Summary

Current bird strike prevention systems in aviation lack the fine-grained capability to identify bird species, flock types, and group sizes, hindering accurate prediction of their flight trajectories and associated risks. This work proposes a multi-level image classification framework based on convolutional neural networks (CNNs) that, for the first time, jointly models these three visual attributes—species, flock type, and group size—and employs dedicated classifiers to simultaneously achieve high-precision recognition of all three. By enabling fine-grained understanding of avian behavior, the proposed method significantly enhances the accuracy of flight path prediction and bird strike risk assessment, thereby advancing the intelligence and effectiveness of bird strike mitigation systems in aviation safety.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• bird strike
• species identification
• flock formation
• flock size
• aviation safety

Innovation

• Convolutional Neural Networks
• bird species classification
• flock formation
• aviation safety
• multi-level classification