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.
Object Detection in Autonomous Maritime Vehicles: Comparison Between YOLO V8 and EfficientDet
Lect. Notes in Networks, Syst.
International Conference on Data Science and Network Engineering ; 2023 ; Agartala, India June 02, 2023 - June 03, 2023
2023-11-03
17 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Object Detection Using Vision Transformed EfficientDet
IEEE | 2023
|Track Surface Defect Detection Based on EfficientDet
Springer Verlag | 2022
|Object Detection for Vehicles with Yolo
IEEE | 2024
|YOLO-Based Object Detection and Tracking for Autonomous Vehicles Using Edge Devices
Springer Verlag | 2022
|Track Surface Defect Detection Based on EfficientDet
British Library Conference Proceedings | 2022
|