  To ensure the security of airports, it is essential to protect the airside
from unauthorized access. For this purpose, security fences are commonly used,
but they require regular inspection to detect damages. However, due to the
growing shortage of human specialists and the large manual effort, there is the
need for automated methods. The aim is to automatically inspect the fence for
damage with the help of an autonomous robot. In this work, we explore object
detection methods to address the fence inspection task and localize various
types of damages. In addition to evaluating four State-of-the-Art (SOTA) object
detection models, we analyze the impact of several design criteria, aiming at
adapting to the task-specific challenges. This includes contrast adjustment,
optimization of hyperparameters, and utilization of modern backbones. The
experimental results indicate that our optimized You Only Look Once v5 (YOLOv5)
model achieves the highest accuracy of the four methods with an increase of
6.9% points in Average Precision (AP) compared to the baseline. Moreover, we
show the real-time capability of the model. The trained models are published on
GitHub: https://github.com/N-Friederich/airport_fence_inspection.
