Countries flags detection based on local context network and color features

Countries flags are characterized by a combination of special colors. Building an automatic country flag detector is a hard task because of many challenges like deformation and difference in point of view. Motivated by the unique feature of the country flag colors and the power of Deep Learning models, we propose to use color-based features and a Convolutional Neural Network (CNN) with a special local context neural network to perform the countries flags detection task. The proposed approach aims to enhance the performance of the ordinary Convolutional Neural Network by adding a local context neural network to enhance the localization task and adding a color-based descriptor to enhance the identification task. The color-based descriptor was used to focus on the color features because of its importance for the studied task. The Convolutional Neural Network was proposed to extract more relevant features for both localization and identification tasks. The local context network was used to localize the flag in the image. In order to train and evaluate the proposed approach, we propose to build a custom dataset for the world countries’ flags. The proposed dataset counts 100 images for each country flag with a total of 20,000 images. The evaluation of the proposed approach proves its efficiency by achieving a mean Average Precision of 89.5% and a real-time processing speed. The achieved results have proved the efficiency of the proposed method. The proposed enhancement was very effective that allows the achievement of high accuracy.