Evaluating Performance of Hardware-Accelerated Vessel Detection Algorithms

作者：Michael Tietz, John Warner, Quinton Davidson, Douglas Carssow · 发表于：Digital Commons - USU (Utah State University) · 年份：2026 · DOI：10.26077/p3mg-cg83 · 研究领域：Computer science、Real-time computing、Remote sensing、Artificial intelligence

Earth observing satellites contribute to the monitoring of maritime activity by enabling large-scale coverage of open water. For example, spacecraft data can be used to monitor and combat Illegal, Unreported, and Unregulated (IUU) fishing that occurs in coastal and international waters across the globe. However, information latencies limit the utility of space-based sensors for operational users who need real-time situational awareness. These limitations are especially pronounced for small spacecraft with limited power generation and downlink capabilities. In recent years, the devices and tools that enable edge computing and near real-time downlink from small spacecraft have improved substantially. Equipped with these technologies, spacecraft can reduce large amounts of raw data into actionable notifications for rapid response. For IUU fishing interdiction, small spacecraft could provide maritime law enforcement with near real-time imagery of the current position of fishing fleets. Current edge computing technology enables hardware acceleration of Artificial Intelligence (AI)-based computer vision models that can fit within the Size, Weight, and Power (SWaP) constraints of small spacecraft platforms. However, limited work has characterized image classification models for maritime applications on edge processing devices that can fit within these SWaP constraints. This paper examines the performance of Convolutional Neural Network (CNN)-based classification models implemented o...