MASHINAVIY O‘QITISH ASOSIDA YO‘LLARDAGI TIRBANDLIK HOLATLARINI TAHLIL QILISHNING INTELLEKTUAL ALGORITMLARI
Keywords:
Algoritimni baholash ko‘rsatkichlari, tirbandlik holati bashorati, XGBoost, Random Forest Classifier, Logistic Regression, LGBMClassifier, YOLOAbstract
Hozirgi vaqtda shaharlarning kengayishi va transport vositalari sonining ortishi tirbandlik muammosini dolzarb masalaga aylantirdi. Ushbu muammoni hal qilishda video tasvirlardan transport vositalarini tanib olish uchun YOLO, SSD va Faster R-CNN kabi ilg‘or algoritmlardan foydalanish muhim rol o‘ynaydi. Bu texnologiyalar transport vositalarining turini, joylashuvini va sonini aniqlashda yuqori aniqlik va tezlik bilan ishlaydi. YOLO (You Only Look Once) algoritmi tasvirni bir marta ko‘rib, transport vositalarining aniqligini va joylashuvini belgilaydi, bu esa real vaqt rejimida samarali ishlash imkonini beradi. SSD (Single Shot MultiBox Detector) esa bir nechta konvolyutsion qatlamlardan foydalanib, yuqori aniqlik bilan transport vositalarini aniqlaydi va real vaqt rejimida ishlaydi. Faster R-CNN (Region-based Convolutional Neural Networks) algoritmi esa region prediktsiyasi va konvolyutsion neyron tarmoqlarni birlashtirib, transport vositalarini aniqlashda yuqori aniqlikni ta’minlaydi, lekin tezligi biroz sekinroq. Video tasvirlardan transport vositalarini aniqlash natijasida hosil bo‘lgan ma’lumotlar to‘plami tirbandlik holatlarini bashorat qilishda foydalaniladi. Bu ma’lumotlar to‘plami transport vositalarining soni, turli vaqtlardagi joylashuvi, harakat yo‘nalishi va tezligi kabi ma’lumotlarni o‘z ichiga oladi. Bu ma’lumotlardan foydalangan holda, tirbandlikni bashorat qilish uchun turli sun’iy intellekt algoritmlari qo‘llaniladi. Ushbu tadqiqot ishida XGBoost, Random Forest Classifier, LGBMClassifier (Light Gradient Boosting Machine Classifier) va Logistic Regression kabi tirbandlikni bashorat qilishda eng ko‘p qo‘llaniladigan algoritmlardan foydalanib tirbandlik holatlari bashorat qilindi hamda natijalar baholanib taqqoslandi.
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