2020/12/02 by Benjamin Smith, Benjamin C. Smith, Smith, Benjamin
Computer Science · #Advanced Neural Network Applications #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimization and Search Problems #Parallel #and Cluster Computing (cs.DC) #cs.CV #cs.DC #cs.LG
paper · pdf · doi:10.48550/arxiv.2012.00953
20 pages
arxiv created 2020/12/02 · openalex publication_date 2020/12/02 · arxiv updated 2020/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correct classification of ships, typically limiting class accuracy scores to 88%. This work explores the tensions between customization strategies, class accuracy rates, training times, and costs in cloud based solutions. We demonstrate how a custom U-Net can achieve 92% class accuracy over a validation dataset and 68% over a target dataset with 90% confidence. We also compare a single node architecture with a parameter server variant whose workers act as a boosting mechanism. The parameter server variant outperforms class accuracy on the target dataset reaching 73% class accuracy compared to the best single node approach. A comparative investigation on the systematic performance of the single node and parameter server variant architectures is discussed with support from empirical findings.