[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120432-en":3,"doc-seo-120432-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120432,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Robust Machine Learning for Computer Vision in Naval Application","This thesis develops a resilient machine learning algorithm for classifying naval images to support surveillance, search, and detection across extensive coastal areas. It addresses label noise caused by random annotation errors or deliberate adversarial attacks that can degrade model accuracy. The proposed method uses Rockafellian Risk Minimization (RRM) with a two-step procedure: tuning neural network weights and manipulating nominal probabilities to isolate corrupted data points. The approach reduces reliance on extensive data cleaning, improving processing efficiency. Experiments apply RRM under multiple parameter configurations on naval datasets and evaluate classification accuracy relative to traditional methods. The work aims to strengthen ship detection robustness for automated maritime surveillance systems.","NAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nROBUST MACHINE LEARNING FOR COMPUTER VISION IN NAVAL APPLICATION  \nby  \nGabriel Custodio Rangel  \nJune 2023  \nThesis Advisor: Eric C. Eckstrand  \nCo-Advisor: Johannes O. Royset  \nSecond Reader: Robert L. Bassett  \nApproved for public release. Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  | Form Approved OMB No. 0704-0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704- 0188) Washington, DC, 20503. |  |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>June 2023 |  | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>ROBUST MACHINE LEARNING FOR COMPUTER VISION IN NAVAL APPLICATION |  |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) Gabriel Custodio Rangel |  |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Approved for public release. Distribution is unlimited. |  |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>This thesis proposes the development of a resilient machine learning algorithm that can classify naval images for surveillance, search, and detection operations in vast coastal areas. However, real-world datasets may be affected by label noise introduced either through random inaccuracies or deliberate adversarial attacks, both of which can negatively impact the accuracy of machine learning models. Our innovative approach employs Rockafellian Risk Minimization (RRM) to combat label noise contamination. Unlike existing methodologies reliant on extensively cleaned datasets, our two-step process involves adjusting neural network weights and manipulating data point nominal probabilities to isolate potential data corruption effectively. This technique reduces the dependency on meticulous data cleaning, thereby promoting more efficient and timeeffective data processing. To validate the efficacy and reliability of the proposed model, we apply RRM in several parameter configurations to naval environment datasets and assess its classification accuracy against traditional methods. By leveraging the proposed model, we aim to bolster the robustness of ship detection models, paving the way for a novel, reliable tool that could improve automated maritime surveillance systems. |  |  |  |  |  |  |  |\n| 14. SUBJECT TERMS\u003Cbr>computer vision, neural networks, stochastic gradient descent, Rockafellian risk minimization |  |  |  |  |  |  | 15. NUMBER OF PAGES\u003Cbr>93 |\n|  |  |  |  |  |  |  | 16. PRICE CODE |\n| 17. SECURITY\u003Cbr>CLASSIFICATION OF REPORT\u003Cbr>Unclassified | 18. SECURITY\u003Cbr>CLASSIFICATION OF THIS PAGE\u003Cbr>Unclassified |  | 19. SECURITY\u003Cbr>CLASSIFICATION OF ABSTRACT\u003Cbr>Unclassified |  |  |  | 20. LIMITATION O","cbCaiuMa4YgyHHpk","https://ap.wps.com/l/cbCaiuMa4YgyHHpk","pdf",2619364,1,93,"English","en",105,"# Abstract\n## Problem: Label noise in real-world naval datasets\n## Proposed approach: Rockafellian Risk Minimization (RRM)\n## Method details: two-step weight and probability adjustment\n## Evaluation: accuracy comparison on naval environment datasets\n## Expected impact: more robust ship detection for maritime surveillance","[{\"question\":\"What problem does the thesis focus on for naval computer vision?\",\"answer\":\"It targets accurate classification of naval images for surveillance, search, and detection in coastal environments. The main challenge is that real-world datasets may contain harmful label noise.\"},{\"question\":\"How does the proposed method address label noise?\",\"answer\":\"It uses Rockafellian Risk Minimization (RRM) to combat contamination from random inaccuracies and deliberate adversarial attacks. The approach isolates potential corrupted data without requiring heavily cleaned datasets.\"},{\"question\":\"How is the model validated and what outcomes are expected?\",\"answer\":\"RRM is applied across several parameter configurations on naval environment datasets, and classification accuracy is assessed against traditional methods. The expected outcome is improved robustness for ship detection models used in automated maritime surveillance.\"}]","Robust Machine Learning for Computer Vision in Naval Application | PDF",1785730068,234,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"robust-machine-learning-for-computer-vision-in-naval-application","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/robust-machine-learning-for-computer-vision-in-naval-application/120432/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis focus on for naval computer vision?","Question",{"text":75,"@type":76},"It targets accurate classification of naval images for surveillance, search, and detection in coastal environments. 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