As a means of transportation, ships have a significant impact on economic, military, and other fields. Due to the booming development of artificial intelligence technology, intelligent processing in the shipping field is also an inevitable trend. This article introduces the design of a ship recognition and load detection system based on deep learning. Throughout text detection and text recognition technology in natural scenes, it can efficiently detect and recognize ship identification information and water level lines in the marine environment. Combined with the ships information database, it can automatically determine the type and load capacity of ships, thereby improving the efficiency of ships management. The ship waterline detection method proposed in this article combines semantic segmentation technology and attention mechanism and uses Newton Leibniz formula for correction. Even in the presence of complex situations such as wind, waves, water stains, and obstacles, accurate reading of waterlines can be achieved.
A Ship Recognition and Load Detection System Based on Deep Learning
Lect. Notes on Data Eng. and Comms.Technol.
International Conference on Cognitive based Information Processing and Applications ; 2023 ; Changzhou, China November 02, 2023 - November 03, 2023
Proceedings of the 3rd International Conference on Cognitive Based Information Processing and Applications—Volume 3 ; Kapitel : 52 ; 613-622
31.05.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework
DOAJ | 2019
|Ship Detection Based on Deep Learning
British Library Conference Proceedings | 2019
|Optical resolution requirements for effective deep learning-based ship recognition
British Library Conference Proceedings | 2023
|Arbitrary-Oriented Ship Detection based on Deep Learning
IEEE | 2022
|