II. INTERNATIONAL CONFERENCE ON INNOVATIVE ENGINEERING APPLICATIONS, Muş, Turkey, 20 - 22 May 2021, pp.591-598
This paper focuses on techniques of blurring advertising objects in video with deep learning. The project
designed and implemented a system that blurs the advertising content of the video with deep learning
methods. Faster R-CNN was used as a deep learning method for detecting and recognizing objects. Many
problems were encountered in this study. First, the 9 different classes were collected to train the system.
After preparing the dataset, Faster RCNN was used to train the system with layered architecture. The
Faster R-CNN method is very effective compared to the other methods R-CNN and Fast R-CNN methods.
The dataset is divided into %20 test and %80 train. After the training, the success rates of the pictures in
the test folder were observed. Different success rates have emerged for each class. Finally, the object
containing the advertising product was blurred after the operations. Gaussian filter was used for
blurring. The colors of the object found with the Gaussian filter are softened. This produces a blurred
image on the screen. The success rates were measured with the obtained images