Automatic Identification of Static Harbor Objects Based on Camera Images from a Highly Autonomous Dredger Ship

Abstract

Possible collisions with port infrastructure are a big challenge in the automation of commercial shipping. The first step to avoiding these collisions is identifying static port infrastructure. To minimize the risk of collisions of automated vessels with port infrastructure, this study aims to develop a model for automatically detecting static harbor objects (quay walls and piles) in port areas using a YOLOv5-based deep learning architecture. The existing architecture is adapted by generating a port-specific image dataset using image obfuscation techniques that simulate real-world operational scenarios, additionally improving robustness. To determine optimal hyperparameters, such as image resolution, batch size, or selection of optimization algorithm, multiple experiments were conducted and evaluated. As the proposed system is used in a time critical environment, the evaluation is performed on the basis of model performance as well as inference time.