Yoğun Evrişimli Sinir Ağı ile Gökyüzü Görüntülerinden Hava Durumu Tespiti

Year 2022, Volume: 12 Issue: 3, 1238 - 1249, 01.09.2022

Abdullah Şener Burhan Ergen

https://doi.org/10.21597/jist.1093732

Abstract

Hava durumu koşulları değiştirilemez bir yapı olarak karşımıza çıkmaktadır. Ancak hava koşullarının tespit edilip belirlenmesi bireylerin fiziksel aktivitelerini planlamasında yardımcı olabilmektedir. Yapılan çalışmada farklı gökyüzü görüntüleri ile hava durumu tespiti işlemlerini son yıllarda bilgisayar alanında popüler çalışma konularından biri olan görüntü sınıflandırma yöntemleriyle gerçekleştirilmeye çalışılmıştır. Çalışmada farklı hava koşullarına ve çözünürlüklere sahip görüntüden oluşan veri seti kullanılmış. Görüntüler üzerinde görüntü işleme teknikleri uygulanarak görüntülerin özellik haritaları çıkarılmıştır. %96.4 doğruluk oranı ile sınıflandırma işlemi gerçekleştirilmiştir. Gerçekleştirilen sınıflandırma sayesinde doğruluk oranı yüksek, kısa zamanlı ve maliyeti düşük hava durumu tespiti gerçekleştirilebilir.

Keywords

Görüntü sınıflandırma, Görüntü İşleme, Veri Arttırma, DenseNet

Weather Detection from Sky Images with Dense Convolutional Neural Network

Abstract

Weather conditions appear as an unchangeable structure. However, determining and determining weather conditions can help individuals plan their physical activities. In the study, it has been tried to perform different sky images and weather detection processes with image classification methods, which is one of the popular work subjects in the computer field in recent years. In the study, a data set consisting of images with different weather conditions and resolutions was used. The number of images in the data set has been increased by using various data augmentation methods. The feature maps of the images were obtained by applying image processing techniques to the images. In the next part of the study, the classification process was carried out on the images with an accuracy rate of 96.4% using the DenseNet image classification model.

Keywords

Image classification, Image Processing, Data Augmentation, DenseNet

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