Automatic Detection and Predictive Geolocation of Foreign Object Debris on Airport Runway

Abstract

The detection and removal of Foreign Object Debris (FOD) on airport runway present significant challenges due to the small objects and the range of complex weather conditions that impact visibility and equipment efficiency. To address the problem, an FOD detection model using Swin Transformer (ST) enhanced YOLOv5 is proposed and a geolocation prediction model based on machine learning regression algorithms is introduced. Subsequently, an Unmanned Aerial Vehicle (UAV) is deployed to acquire a dataset with 74,737 images featuring 21 distinct types of objects. Furthermore, the model is trained using a comprehensive dataset of FOD on airport runway. By integrating a self-attention model with Convolutional Neural Network (CNN), the FOD detection model is formulated, yielding promising outcomes. The ablation tests demonstrate varying degrees of enhancement in the Mean Average Precision (mAP) value across each part of the model. Comparative analyses against established algorithms including YOLOv5, YOLOX, and YOLOv7 reveal superior overall performance and enhanced capability in detecting small objects. Notably, the proposed model exhibits lower relative accuracy decay when presented with diverse input data. Additionally, FOD geolocation prediction tests underscore the effectiveness of the machine learning-based regression algorithm, highlighting its substantial potential for practical implementation.

Keywords

• Airport FOD
• deep learning
• geolocation prediction
• object detection
• YOLOv5-Swin transformer