{"id":3352,"date":"2024-11-10T19:30:18","date_gmt":"2024-11-10T18:30:18","guid":{"rendered":"https:\/\/www.sasa-aerospace.it\/flight-team\/?post_type=blog_posts&p=3352"},"modified":"2025-05-13T06:38:02","modified_gmt":"2025-05-13T04:38:02","slug":"test-yolov11l-cosa-abbiamo-imparato","status":"publish","type":"blog_posts","link":"https:\/\/www.sasa-aerospace.it\/flight-team\/blog_posts\/test-yolov11l-cosa-abbiamo-imparato\/","title":{"rendered":"Test YOLOv11l: Cosa abbiamo imparato"},"content":{"rendered":"

Nel <\/span>percorso <\/span>di <\/span>sviluppo <\/span>del <\/span>nostro <\/span>UAV <\/span>autonomo, <\/span>il <\/span>Sapienza <\/span>Flight <\/span>Team<\/span><\/em> ha <\/span>recentemente <\/span>condotto <\/span>una <\/span>serie <\/span>di <\/span>test <\/span>approfonditi <\/span>utilizzando <\/span>il <\/span>modello <\/span>YOLOv11l<\/span><\/strong> su <\/span>un <\/span>dataset <\/span>sintetico<\/span><\/strong> progettato <\/span>per <\/span>simulare <\/span>le <\/span>condizioni <\/span>operative <\/span>del <\/span>drone.<\/span><\/p>\n

L\u2019obiettivo <\/span>iniziale <\/span>era <\/span>valutare <\/span>la <\/span>capacit\u00e0 <\/span>del <\/span>modello <\/span>di <\/span>rilevare <\/span>correttamente <\/span>oggetti <\/span>rilevanti <\/span>per <\/span>la <\/span>navigazione <\/span>autonoma \u2014 <\/span>come <\/span>ostacoli, <\/span>landmark <\/span>e <\/span>marker \u2014 <\/span>in <\/span>un <\/span>ambiente <\/span>controllato, <\/span>ma <\/span>realistico. <\/span>Il <\/span>dataset <\/span>\u00e8 <\/span>stato <\/span>generato <\/span>artificialmente <\/span>per <\/span>accelerare <\/span>il <\/span>processo <\/span>di <\/span>raccolta <\/span>dati <\/span>e <\/span>permettere <\/span>una <\/span>maggiore <\/span>variet\u00e0 <\/span>di <\/span>scenari <\/span>in <\/span>tempi <\/span>rapidi.<\/span><\/p>\n

Tuttavia, <\/span>durante <\/span>l\u2019analisi <\/span>dei <\/span>risultati, <\/span>abbiamo <\/span>osservato <\/span>performance <\/span>inferiori <\/span>rispetto <\/span>alle <\/span>attese: <\/span>YOLOv11l, <\/span>pur <\/span>essendo <\/span>un <\/span>modello <\/span>avanzato, <\/span>faticava <\/span>a <\/span>generalizzare <\/span>su <\/span>immagini <\/span>con <\/span>caratteristiche <\/span>visive <\/span>leggermente <\/span>diverse <\/span>da <\/span>quelle <\/span>viste <\/span>in <\/span>training, <\/span>anche <\/span>se <\/span>generate <\/span>dallo <\/span>stesso <\/span>simulatore.<\/span><\/p>\n

Questa <\/span>scoperta <\/span>ha <\/span>messo <\/span>in <\/span>luce <\/span>l’importanza <\/span>cruciale <\/span>dei <\/span>modelli <\/span>visivi <\/span>utilizzati <\/span>nel <\/span>dataset <\/span>sintetico<\/span>. <\/span>Abbiamo <\/span>compreso <\/span>che <\/span>le <\/span>texture, <\/span>le <\/span>illuminazioni <\/span>e <\/span>le <\/span>variazioni <\/span>delle <\/span>scene <\/span>erano <\/span>troppo <\/span>uniformi <\/span>o <\/span>poco <\/span>realistiche, <\/span>portando <\/span>il <\/span>modello <\/span>ad <\/span>apprendere <\/span>pattern <\/span>troppo <\/span>specifici <\/span>e <\/span>poco <\/span>robusti.<\/span><\/p>\n

Questa <\/span>esperienza <\/span>ci <\/span>ha <\/span>spinti <\/span>a <\/span>riflettere <\/span>su <\/span>due <\/span>fronti:<\/span><\/p>\n

    \n
  1. \n

    Migliorare <\/span>la <\/span>qualit\u00e0 <\/span>e <\/span>la <\/span>variet\u00e0 <\/span>dei <\/span>dati <\/span>sintetici<\/span><\/strong>, <\/span>usando <\/span>tecniche <\/span>come <\/span>il <\/span>domain <\/span>randomization<\/span><\/em> e <\/span>l’uso <\/span>di <\/span>motori <\/span>grafici <\/span>pi\u00f9 <\/span>avanzati;<\/span><\/p>\n<\/li>\n

  2. \n

    Esplorare <\/span>modelli <\/span>alternativi <\/span>a <\/span>YOLO<\/span><\/strong>, <\/span>potenzialmente <\/span>pi\u00f9 <\/span>adatti <\/span>a <\/span>contesti <\/span>con <\/span>scarse <\/span>generalizzazioni, <\/span>come <\/span>gli <\/span>anchor-<\/span>free <\/span>detectors<\/span><\/em> o <\/span>modelli <\/span>che <\/span>integrano <\/span>meccanismi <\/span>di <\/span>attenzione <\/span>spaziale <\/span>e <\/span>temporale.<\/span><\/p>\n<\/li>\n<\/ol>\n

    In <\/span>sintesi, <\/span>i <\/span>test <\/span>con <\/span>YOLOv11l <\/span>ci <\/span>hanno <\/span>insegnato <\/span>che <\/span>non <\/span>basta <\/span>un <\/span>modello <\/span>potente <\/span>se <\/span>i <\/span>dati <\/span>non <\/span>lo <\/span>sfidano <\/span>abbastanza <\/span>durante <\/span>l\u2019addestramento<\/span>. <\/span>\u00c8 <\/span>un <\/span>equilibrio <\/span>delicato <\/span>tra <\/span>architettura <\/span>e <\/span>dati, <\/span>e <\/span>stiamo <\/span>gi\u00e0 <\/span>lavorando <\/span>per <\/span>trovare <\/span>la <\/span>combinazione <\/span>giusta <\/span>per <\/span>il <\/span>volo <\/span>autonomo.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    Nel percorso di sviluppo del nostro UAV autonomo, il Sapienza Flight Team ha recentemente condotto una serie di test approfonditi utilizzando il modello YOLOv11l su un dataset sintetico progettato per simulare le condizioni operative del drone. L\u2019obiettivo iniziale era valutare la capacit\u00e0 del modello di rilevare correttamente oggetti rilevanti per la navigazione autonoma \u2014 come […]<\/p>\n","protected":false},"author":11,"featured_media":3357,"template":"","class_list":["post-3352","blog_posts","type-blog_posts","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nTest YOLOv11l: Cosa abbiamo imparato - SASA Flight Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.sasa-aerospace.it\/flight-team\/blog_posts\/test-yolov11l-cosa-abbiamo-imparato\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Test YOLOv11l: Cosa abbiamo imparato - SASA Flight Team\" \/>\n<meta property=\"og:description\" content=\"Nel percorso di sviluppo del nostro UAV autonomo, il Sapienza Flight Team ha recentemente condotto una serie di test approfonditi utilizzando il modello YOLOv11l su un dataset sintetico progettato per simulare le condizioni operative del drone. 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