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Related Experiment Video

Updated: Jul 12, 2026

Visually Mediated Odor Tracking During Flight in Drosophila

Visually Mediated Odor Tracking During Flight in Drosophila

Published on: January 26, 2009

Airport-FOD3S: A Three-Stage Detection-Driven Framework for Realistic Foreign Object Debris Synthesis.

Hanglin Cheng¹, Yihao Li¹, Ruiheng Zhang^1,2

¹School of Transportation, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This study enhances Foreign Object Debris (FOD) detection by using advanced AI models for realistic image generation and a novel blending technique. Results show significantly improved detection accuracy, crucial for safety-critical environments.

Keywords:

foreign object debris image blending image generation object detection size transformation

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Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

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Published on: December 29, 2021

Area of Science:

Computer Vision
Artificial Intelligence
Aerospace Engineering

Background:

Traditional Foreign Object Debris (FOD) detection methods struggle with large datasets and low accuracy.
Effective FOD detection is critical for preventing accidents in aviation and other industries.

Purpose of the Study:

To develop advanced data augmentation techniques for FOD detection.
To improve the accuracy and robustness of FOD detection algorithms.

Main Methods:

Utilized generative adversarial networks (GANs) and diffusion models for image data augmentation.
Proposed a three-stage image blending method incorporating size transformation, seamless processing, and style transfer.
Evaluated image quality using metrics like SSIM, PSNR, and Depthanything.
Tested object detection models (Faster R-CNN, YOLOv8, YOLOv11) with a similarity distance strategy (SimD).

Main Results:

Generated realistic FOD images under diverse environmental conditions.
The proposed three-stage blending method achieved superior image quality (SSIM=0.99, PSNR=45 dB).
YOLOv11 with SimD, trained on augmented data, reached a mean Average Precision (mAP) of 86.95%.

Conclusions:

Data augmentation and the SimD strategy significantly enhance FOD detection accuracy.
The developed methods offer a promising solution for overcoming limitations in traditional FOD detection.