[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120514-en":3,"doc-seo-120514-105":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},120514,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","COLLISION AVOIDANCE INFORMATION SYSTEM UTILIZING MACHINE LEARNING IMAGE REGONITION - Thesis - Master of Mining Engineering","This thesis tackles safety risks caused by powered haulage fatalities in underground mining through the development and evaluation of a machine learning-driven Collision Avoidance Information System (CAIS). Using a ZED 2i camera, the research captures RGB and depth data to improve spatial awareness in visually constrained underground environments. A custom dataset of underground mining equipment is collected from limestone and zinc mines, annotated, and used to train a segmentation network. Field tests show 82% accuracy in a limestone setting and 45% in a zinc setting, indicating the need for environment-specific training. Results support real-time hazard detection and operator awareness, with emphasis on expanding datasets and refining models to improve CAIS reliability across sites and move toward zero mining fatalities.","University of Kentucky  \nUKnowledge  \n\n| Theses and Dissertations--Mining Engineering | Mining Engineering |\n| --- | --- |\n\n2025  \nCOLLISION AVOIDANCE INFORMATION SYSTEM UTILIZING MACHINE LEARNING IMAGE REGONITION  \nMichael W. Long  \nUniversity of Kentucky, [michaelwynnlong@yahoo.com](michaelwynnlong@yahoo.com)[ ](michaelwynnlong@yahoo.com)[Author ORCID Identifier:](Author ORCID Identifier:)[ ](Author ORCID Identifier:)[https://orcid.org/0009-0000-5374-5960](https://orcid.org/0009-0000-5374-5960)  \nDigital Object Identifier: [https://doi.org/10.13023/etd.2025.53](https://doi.org/10.13023/etd.2025.53)  \nRight click to open a feedback form in a new tab to let us know how this document benefits you.  \nRecommended Citation  \nLong, Michael W., \"COLLISION AVOIDANCE INFORMATION SYSTEM UTILIZING MACHINE LEARNING IMAGE REGONITION\" (2025) . Theses and Dissertations--Mining Engineering. 88.  \n[https://uknowledge.uky.edu/mng_etds/88](https://uknowledge.uky.edu/mng_etds/88)  \nThis Master's Thesis is brought to you for free and open access by the Mining Engineering at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Mining Engineering by an authorized administrator of UKnowledge. For more information, [please contact UKnowledge@lsv.uky.edu](please contact UKnowledge@lsv.uky.edu), [rs_kbnotifs-acl@uky.edu](rs_kbnotifs-acl@uky.edu).  \nSTUDENT AGREEMENT:  \nI represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File.  \nI hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless an embargo applies.  \nI retain all other ownership rights to the copyright of my work. I also retain the right to use in future works (such as articles or books) all or part of my work. I understand that I am free to register the copyright to my work.  \nREVIEW, APPROVAL AND ACCEPTANCE  \nThe document mentioned above has been reviewed and accepted by the student’s advisor, on behalf of the advisory committee, and by the Director of Graduate Studies (DGS), on behalf of the program; we verify that this is the final, approved version of the student’s thesis including all changes required by the advisory committee. The undersigned agree to abide by the statements above.  \nMichael W. Long, Student  \nSteven Schafrik, Major Professor Joseph Sottile, Director of Graduate Studies  \nCOLLISION AVOIDANCE INFORMATION SYSTEM UTILIZING MACHINE  \nLEARNING IMAGE REGONITION  \nTHESIS  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Mining Engineering in the  \nCollege of Engineering  \nat the University of Kentucky  \nBy  \nMichael Wynn Long  \nLexington, Kentucky  \nDirector: Dr. Steven J. Schafrik, Professor of Mining Engineering  \nLexington, Kentucky  \n2025  \nCopyright © Michael Wynn Long 2025 [https://orcid.org/0009-0000-5374-5960](https://orcid.org/0009-0000-5374-5960)  \nABSTRACT OF THESIS  \nCOLLISION AVOIDANCE INFORMATION SYSTEM UTILIZING MACHINE  \nLEARNING IMAGE REGONITION  \nThis thesis addresses significant safety challenges presented by powered haulage fatalities in underground mining by developing and evaluating a machine learning-driven Collision Avoidance Information System (CAIS) . The research utilized a ZED 2i camera to capture both RGB and depth data for enhanced spatial awareness in visually limited undergro","cbCainZbMD5s2S7V","https://ap.wps.com/l/cbCainZbMD5s2S7V","pdf",2408996,1,60,"English","en",105,"# Abstract of Thesis\n## Collision Avoidance Information System (CAIS)\n## Data Collection and Dataset Annotation\n## Model Training and Segmentation Network\n## Field Testing and Accuracy Results\n## Findings and Future Work","[{\"question\":\"What problem does the Collision Avoidance Information System (CAIS) address?\",\"answer\":\"It addresses safety challenges from powered haulage fatalities in underground mining by providing real-time collision hazard detection and operator awareness.\"},{\"question\":\"How does the thesis collect data for the CAIS?\",\"answer\":\"It uses a ZED 2i camera to capture both RGB and depth data, and collects a specialized, annotated dataset of underground mining equipment from limestone and zinc mines.\"},{\"question\":\"What were the field-testing accuracy results and what do they imply?\",\"answer\":\"The CAIS reached 82% accuracy in a limestone mine similar to the training data, but 45% accuracy in a zinc mine with different equipment, indicating a need for environment-specific training.\"}]","COLLISION AVOIDANCE INFORMATION SYSTEM UTILIZING MACHINE LEARNING IMAGE REGONITION - Thesis - Master of Mining Engineering | PDF",1785730442,151,{"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},"collision-avoidance-information-system-utilizing-machine-learning-image-regonition-thesis-master-of-mining-engineering","",{"@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/collision-avoidance-information-system-utilizing-machine-learning-image-regonition-thesis-master-of-mining-engineering/120514/",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 Collision Avoidance Information System (CAIS) address?","Question",{"text":75,"@type":76},"It addresses safety challenges from powered haulage fatalities in underground mining by providing real-time collision hazard detection and operator awareness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis collect data for the CAIS?",{"text":80,"@type":76},"It uses a ZED 2i camera to capture both RGB and depth data, and collects a specialized, annotated dataset of underground mining equipment from limestone and zinc mines.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the field-testing accuracy results and what do they imply?",{"text":84,"@type":76},"The CAIS reached 82% accuracy in a limestone mine similar to the training data, but 45% accuracy in a zinc mine with different equipment, indicating a need for environment-specific training.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":21,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]