[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125656-en":3,"doc-seo-125656-105":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},125656,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Ensemble Machine Learning Model for Automated Asteroid Detection","Near Earth Objects (NEO) pose a continual risk that depends on timely sky surveys to identify potentially dangerous asteroids and estimate their future impact odds. Existing efforts include human-driven and automated projects such as EURONEAR and the EURONEAR blink mini-survey that evolved into the semi-automatic NEARBY system using image processing and service-oriented methods. This paper proposes extending NEARBY with an ensemble of three state-of-the-art machine learning models for binary asteroid presence classification. Evaluations on 11,000 real images show the ensemble recovers about 55% of asteroids missed by NEARBY while achieving 0.88 recall for asteroids already detected, boosting combined detection from 89% to 95%.","ENSEMBLE MACHINE LEARNING MODEL FOR AUTOMATED  \nASTEROID DETECTION  \nRAUL URECHIATU 1 , MARC FRINCU2 , OVIDIU VADUVESCU3 , COSTIN BOLDEA4  \n1 Department of Computer Science, Faculty of Mathematics and Computer Science, West University of  \nTimisoara, Timisoara, Romania Email: [raul.urechiatu97@e-uvt.ro](raul.urechiatu97@e-uvt.ro)  \n2 Department of Computer Science, School of Science and Technology, Nottingham Trent University,  \nNottingham, [United Kingdom Email: marc.frincu@ntu.ac.uk](United Kingdom Email: marc.frincu@ntu.ac.uk)  \n3 Isaac Newton Group (ING), [Apt. de](Apt. de) correos 321, Santa Cruz de La Palma, Canary Islands, Spain  \nEmail: [ovidiu.vaduvescu@gmail.com](ovidiu.vaduvescu@gmail.com)  \n4 Department of Computer Science, Faculty of Sciences, University of Craiova, Craiova, Romania  \nEmail: cboldea@inf.ucv.ro Email: [ovidiu.vaduvescu@gmail.com](ovidiu.vaduvescu@gmail.com)  \nAbstract. The potential threat of Near Earth Objects (NEO) requires a constant survey of the night sky to discover potentially dangerous objects and assess their future impact odds. Several ongoing surveys relying on human operators or automated techniques exist. One such example is the EURONEAR blink mini-survey project which overtime developed from a pure manual approach to detecting asteroids to semi-automatic methods (NEARBY) using image processing and service-oriented approaches. In this paper, we propose an extension of NEARBY based on an ensemble model comprising three state-of-art machine learning models, some used in similar approaches. The proposed model is designed for a binary classification problem where candidate images may contain an asteroid in their center. Validation on a real-life dataset comprising  \n11,000 images shows that our ensemble model is capable of recovering about 55% of the asteroids missed by the previous NEARBY automated process while at the sametime having a 0.88 recall on the asteroids already detected by NEARBY. Used together with NEARBY our model increased the detection rate from 89% to 95% .  \nKey words: Asteroids – Machine Learning – Ensemble Models – Automation.  \n1. INTRODUCTION  \nThe potential threat of Near Earth Objects (NEO) requires a constant survey of the night sky to prevent future impacts. Existing NEO projects such as EURONEAR (Vaduvescu and Curelaru, 2006) work towards identifying new NEOs or refining the orbits of existing potentially hazardous asteroids. Recent research concluded that there are about 1,000 NEOs larger than 1 km and up to about 70,000 NEOs larger than 100 m all with a trajectory taking them closer than 50 million km from Earth (Tricarico, 2017; Granvik et al., 2016; Harris and D'Abramo, 2015) . Out of these, above 30,000 are currently being tracked (NASA-JPL, 2022) .  \nOver the last decade, several pipelines for automated detection and processing of moving targets have been implemented. Recent examples include NEARBY (Ste  \nRomanian Astron. J. , Vol. 1, No. 1, p. 1–16, Bucharest, 2019  \nfanut et al., 2018; NEARBY, 2021) and Umbrella (Stnescu and Vduvescu, 2021) . The latter study provides a useful comparison between various approaches (Astrometrica manual and automatic, NEARBY, and Umbrella) on 15 different Wide Field Camera fields obtained from Isaac Newton Group of Telescopes with results showing that the detection accuracy depends on the dataset (and implicitly seeing conditions) with no clear best method.  \n1.1. RELATED WORK  \nThese automated methods rely on image processing techniques without learning from past examples as human operators do. This leads to an increase in the number of false positives (FP) (i.e. , detected objects wrongly identified as asteroids) and false negatives (FN) (i.e. , missed NEOs) . As a result, a Machine Learning (ML) algorithm capable of learning from previous examples could help reduce the number of false detections and provide results closer to that of a human operator. Unfortunately, not much work has been done in terms of ML for asteroid det","cbCaitJXD0k7tgbB","https://ap.wps.com/l/cbCaitJXD0k7tgbB","pdf",1619390,1,16,"English","en",105,"# Introduction\n## Related Work\n## Proposed Solution and Integration in NEARBY","[{\"question\":\"为什么需要自动化小行星检测与近地天体（NEO）监测？\",\"answer\":\"NEO 可能带来未来撞击风险，因此需要持续观测来发现潜在危险天体并评估其后续撞击概率。\"},{\"question\":\"NEARBY 系统在自动检测流程中扮演什么角色？\",\"answer\":\"NEARBY 是基于天文图像处理平台的自动检测软件，会从图像中提取候选元素并识别可能的小行星轨迹，生成待人工复核的潜在目标列表。\"},{\"question\":\"所提出的集成模型带来了哪些性能提升？\",\"answer\":\"在包含 11,000 张真实图像的数据集上，集成模型能补回约 55% 被先前 NEARBY 流程漏掉的天体，同时对 NEARBY 已检测到的小行星实现 0.88 的召回率；与 NEARBY 联用后检测率从 89% 提升到 95%。\"}]","Ensemble Machine Learning Model for Automated Asteroid Detection | PDF",1785900480,40,{"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},"ensemble-machine-learning-model-for-automated-asteroid-detection","",{"@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/ensemble-machine-learning-model-for-automated-asteroid-detection/125656/",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","2026-08-05",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},"为什么需要自动化小行星检测与近地天体（NEO）监测？","Question",{"text":75,"@type":76},"NEO 可能带来未来撞击风险，因此需要持续观测来发现潜在危险天体并评估其后续撞击概率。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"NEARBY 系统在自动检测流程中扮演什么角色？",{"text":80,"@type":76},"NEARBY 是基于天文图像处理平台的自动检测软件，会从图像中提取候选元素并识别可能的小行星轨迹，生成待人工复核的潜在目标列表。",{"name":82,"@type":73,"acceptedAnswer":83},"所提出的集成模型带来了哪些性能提升？",{"text":84,"@type":76},"在包含 11,000 张真实图像的数据集上，集成模型能补回约 55% 被先前 NEARBY 流程漏掉的天体，同时对 NEARBY 已检测到的小行星实现 0.88 的召回率；与 NEARBY 联用后检测率从 89% 提升到 95%。","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","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"]