Object Detection in Autonomous Maritime Vehicles: Comparison Between YOLO V8 and EfficientDet

Autonomous vehicles are becoming more common in various industries, but the use of autonomous maritime vehicles is still being studied. This is because controlling these vehicles requires making important decisions about design, propulsion, payload management, and communication systems, which can lead to errors and collisions. One major challenge is detecting other ships and objects in real-time to avoid collisions. Recently, deep learning techniques based on convolutional neural networks (CNNs) have been developed to help with this challenge, such as YOLOv8 (You Only Look Once) and EfficientDet. This paper examines how these methods can be used to detect ships. We trained and tested these two models on a large maritime dataset. On examining the performance of the two models, we have compared the working of both.