Night Vision, Day & Night Prediction with Object Recognition (NVDANOR) Model

Night vision has been one of the key developments in Computer Vision system as it gave us a key point to modify an area where humans have the least ability to perform. Object detection is reliable and efficient tool to recognize objects in scenarios such as daytime images where the illumination is great. However, night pictures tend to be challenging to recognize for human being and it usually brings us less data than the images that are taken during day due to poor contrast against its background that interfere with clearly recognizing and labeling them. Different models have been proposed for night vision image processing which use denoising, deblurring and enhancing technique however, other methods can be used in order to enhance that picture and make them as usable and understandable as possible. In addition, different prediction methods and models have been developed in order to achieve different degrees of object recognition in that image, still those results and accuracy can be improved for better results. In this paper, we propose a model that can predict which time of the day it is in the picture with help of calculating average brightness on the images of different time periods with HSV. The model includes ResNet-50 and VGG-16 classifiers that can also recognize the objects and buildings in the image with good accuracy. Implementation of deep learning algorithms and image brightness enhancement tools helped us to achieve improved accuracy and better prediction. The model achieved 94% prediction results when it comes to day and night prediction and 93.75% in object detection on night images.