{"id":37004,"date":"2019-10-31T22:15:15","date_gmt":"2019-10-31T19:15:15","guid":{"rendered":"https:\/\/prohoster.info\/blog\/nejroset-beeline-ai-poisk-lyudej-pomozhet-najti-propavshih\/"},"modified":"2019-10-31T22:15:15","modified_gmt":"2019-10-31T19:15:15","slug":"nejroset-beeline-ai-poisk-lyudej-pomozhet-najti-propavshih","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/news\/nejroset-beeline-ai-poisk-lyudej-pomozhet-najti-propavshih","title":{"rendered":"La rete neurale \"Beeline AI \u2014 Ricerca Persone\" ti aiuter\u00e0 a trovare persone scomparse","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\u00abBeeline\u00bb ha sviluppato una rete neurale specializzata che aiuter\u00e0 nella ricerca di persone scomparse: la piattaforma si chiama \u00abBeeline AI \u2014 Ricerca persone\u00bb.<\/p>\n

La soluzione mira a semplificare il lavoro della squadra di ricerca e soccorso \u00abLisa Alert<\/a><\/noindex>\u00bb. Dal 2018, questo team utilizza droni per operazioni di ricerca condotte in boschi e aree industriali delle citt\u00e0. Tuttavia, l'analisi delle immagini ottenute dalle telecamere dei droni richiede il coinvolgimento di un gran numero di volontari. Inoltre, ci vuole molto tempo.<\/p>\n

\"La<\/p>\n

La rete neurale \u00abBeeline AI \u2014 Ricerca persone\u00bb \u00e8 stata progettata per automatizzare il processo di elaborazione delle fotografie. Si afferma che gli algoritmi specializzati consentono di ridurre il tempo di esame e classificazione delle immagini ricevute di ben due volte e mezzo.<\/p>\n

La piattaforma utilizza tecnologie di reti neurali convoluzionali, il che aumenta l'efficacia degli strumenti di visione artificiale. L'addestramento della rete neurale \u00e8 stato effettuato su set di immagini reali. I test hanno dimostrato che la precisione del modello sulle immagini di test si avvicina al 98%.<\/p>\n

<\/center><\/p>\n

Il compito principale di \u00abBeeline AI \u2014 Ricerca persone\u00bb \u00e8 la classificazione delle fotografie \u00abvuote\u00bb e non informative, in cui non si vede chiaramente una persona o attributi che indicano che in quel luogo c'era stata una persona. Questo consente al gruppo di analisi di concentrarsi immediatamente sulle immagini potenzialmente utili.<\/p>\n

\"La<\/p>\n

Il sistema \u00e8 in grado di adattarsi a diverse condizioni. Trova oggetti con la stessa precisione sia da altezze di 30-40 metri che da 100 metri. Inoltre, la rete neurale \u00e8 in grado di elaborare immagini con un alto livello di \u00abrumore\u00bb visivo: alberi, paesaggi naturali, crepuscolo, ecc.<\/p>\n

\u00abPotenzialmente, la rete neurale \u00e8 in grado di trovare persone e oggetti in tutti i luoghi di ricerca, come boschi, paludi, campi, citt\u00e0, indipendentemente dalla stagione e dall'abbigliamento della persona, poich\u00e9 l'algoritmo \u00e8 progettato per funzionare in qualsiasi momento dell'anno e potenzialmente sar\u00e0 in grado di riconoscere posizioni corporee non standard nello spazio, come una persona seduta, sdraiata o parzialmente coperta dalla vegetazione\u00bb, osserva \u00abBeeline\u00bb. <\/p>\n

<\/p>\n

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\t\t\t\t


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Fonte: 3dnews.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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