Frontiers in Marine Science | |
Cross-sensor vision system for maritime object detection | |
Marine Science | |
Steven J. Simske1  Vinay Mohan1  | |
[1] Department of Systems Engineering, Colorado State University, Fort Collins, CO, United States; | |
关键词: deep learning; vessel detection system; maritime vessel; optical satellite system; object detection; convolutional neural network; synthetic aperture radar; | |
DOI : 10.3389/fmars.2023.1112955 | |
received in 2022-12-06, accepted in 2023-02-13, 发布年份 2023 | |
来源: Frontiers | |
【 摘 要 】
Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.
【 授权许可】
Unknown
Copyright © 2023 Mohan and Simske
【 预 览 】
Files | Size | Format | View |
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RO202310109011669ZK.pdf | 925KB | download |