Görme engelliler için nesne tanıma ve resim altyazısını derin öğrenme teknikleriyle entegre eden verimli bir aktivite tanıma modeli

Yıl 2024, , 2177 - 2186, 20.05.2024

Zeynep Hilal Kilimci , Ayhan Küçükmanisa

https://doi.org/10.17341/gazimmfd.1245400

Öz

Bir görüntünün içeriğini otomatik olarak tanımlamak, bilgisayarla görmeyi ve doğal dil işlemeyi birbirine bağlayan yapay zekadaki temel bir görevdir. Bu çalışmada, bilgisayarla görü ve makine çevirisindeki son gelişmeleri birleştiren ve bir görüntüyü tanımlayan doğal cümleler oluşturmak için derin ve tekrarlayan bir mimariye dayalı üretken bir model sunulmuştur. Oluşturulan bu model ile görüntülerden elde edilen metinler, ses dosyası formatına dönüştürülebilmekte ve görme engelli insanlar için kişinin etrafında bulunan nesnelerin aktivitesi tanımlanabilmektedir. Bu amaçla, ilk olarak, belirli bir görüntüdeki bir veya daha fazla nesnenin varlığını, konumunu ve türünü tanımlayan YOLO modeliyle görüntüler üzerinde nesne tanıma işlemi gerçekleştirilmiştir. Sonrasında, uzun kısa dönem hafıza ağları (LSTM) eğitim görüntüsü verilen hedef açıklama cümlesinin olasılığını en üst düzeye çıkarmak için eğitilmiştir. Böylece, ilgili görüntü içerisinde yer alan aktiviteler, açıklama olarak metin biçimine dönüştürülmüştür. Metin biçimine dönüştürülen aktiviteler, Google metin okuma platformundan faydalanılarak aktiviteyi tanımlayan ses dosyaları elde edilmiştir. Önerilen modelin etkinliğini göstermek amacıyla dört farklı özellik enjeksiyon mimarisi değerlendirilerek Flickr8K, Flickr30K ve MSCOCO veri kümeleri kullanılmıştır. Deney sonuçları, önerdiğimiz modelin görme engelli bireyler için aktivite tanımlamayı sesli olarak ifade etmede başarılı olduğunu göstermiştir.

Anahtar Kelimeler

Aktivite tanıma, derin öğrenme modelleri, görüntü altyazısı üretici, özellik enjeksiyon teknikleri, uzun kısa dönem hafıza ağları.

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