[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119938-en":3,"doc-seo-119938-105":30,"detail-sidebar-cat-0-en-105":84},{"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":20,"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},119938,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","ROBUST MACHINE LEARNING FOR COMPUTER VISION IN NAVAL APPLICATION","This thesis proposes the development of a resilient machine learning algorithm for classifying naval images used in surveillance, search, and detection across vast coastal areas. Real-world datasets may suffer from label noise caused by random inaccuracies or deliberate adversarial attacks, degrading model accuracy. The approach employs Rockafellian Risk Minimization (RRM) to counter label-noise contamination. The method uses a two-step process that adjusts neural network weights and manipulates nominal data point probabilities to isolate suspected corrupted samples. Validation is conducted by applying RRM under multiple parameter configurations to naval environment datasets and comparing classification accuracy with traditional methods. The goal is to strengthen ship detection models and enable a more reliable automated maritime surveillance capability.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2023-06\u003Cbr>ROBUST MACHINE LEARNING FOR COMPUTER VISION IN NAVAL APPLICATION\u003Cbr>Custodio Rangel, Gabriel\u003Cbr>Monterey, CA; Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/72150](https://hdl.handle.net/10945/72150) |\n\nNPS Scholarship Theses  \nCopyright is reserved by the copyright owner.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL 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 time-effective 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, n","cbCaiqF8181DGEhi","https://ap.wps.com/l/cbCaiqF8181DGEhi","pdf",4158602,1,95,"English","en",105,"# Abstract\n# Proposed Method: Rockafellian Risk Minimization\n## Two-Step Training Strategy\n# Experimental Validation and Comparison\n## Parameter Configurations\n## Accuracy Assessment\n# Intended Impact on Maritime Surveillance","[{\"question\":\"How is the approach validated?\",\"answer\":\"RRM is applied using several parameter configurations on naval environment datasets, and classification accuracy is evaluated against traditional methods.\"}]","ROBUST MACHINE LEARNING FOR COMPUTER VISION IN NAVAL APPLICATION | PDF",1785727094,239,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":28},"robust-machine-learning-for-computer-vision-in-naval-application","",{"@graph":36,"@context":78},[37,54,69],{"@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/119938/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"How is the approach validated?","Question",{"text":76,"@type":77},"RRM is applied using several parameter configurations on naval environment datasets, and classification accuracy is evaluated against traditional methods.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,103,108,113,116,121,124,128],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":46,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},6,"Technology",50,"technology",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":114,"slug":115},30,"research-report",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},9,"Religion & Spirituality",20,"religion-spirituality",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":119,"slug":123},"World Cup","world-cup",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":125,"slug":127},10,"Lifestyle","lifestyle",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":99,"slug":131},19,"General","general"]