## 4 Comparative Analysis of RPLC with CNN through Experimental Validation
### Preparation and Experimental Setup  
For experimental setup, the image data is prepared in two different set of images along with the annotations representing Dark, Semi-dark and Bright images. The two sets comprise of a total of 300 and 900 images, vice versa. These set of images, as mentioned earlier, are extracted from ExDark dataset and MSCOCO dataset, both these datasets comprise of variant sized images. To handle the variability of image sizes we considered two different scenarios representing the size of images. The scenarios under consideration are rescaled versions of the images. The first scenario with resolution of 100 by 100 size, and for the second scenario scaled version of 640 by 640 is considered with the zero padding. The addition of zero padding is employed, so that the stat of the art approaches used for validation, i.e., basic CNN and SVM obtains the same size of images of training, testing and validation. However, our proposed algorithm, implying minimum conditions for classification, can classify the images as they appear with whatever the resolution of image is given as input. In addition to that, the proposed algorithm is robust and simplistic enough to work with small amount of image data and large amounts as well. Whereas, for basic CNN to work, we divided the images set in further subsets for training, testing and validation sets. The subsets for image set having a total of 300 is divided into 225 images for training, 45 images for testing, and 25 images for validation set each. The subsets for image set having a total of 900 is divided into 675 images for training, 135 images for testing, and 90 images for validation set each. To increase the dataset size for CNN training to infuse versatility and better training, python library Keras has been used. The ImageDataGenerator method provide by Keras applies data augmentation steps to increase the dataset size and thereby integrates versatility in the network. The data augmentation strategies are applied on all the phases training, testing and validation such that the augmented training images do not vary with a huge margin as compared to the testing and validation images. The augmentation applied on all--training, testing, and validation--maintains the uniformity throughout the experiment. For the augmentation, image pixel normalization is employed to standardize pixel values for fitting and evaluating the CNN. Augmentation methods such as vertical shift, horizontal shift, shear, zoom, and horizontal flips are used. The resultant augmented dataset size is \(\approx\)21,600 images, based on the actual number of samples divided by the batch size for the network, i.e., 900/15 equals 60 images per epoch and the total epochs are 60. Moreover, we have six different augmentation operations, the final number of images becomes approximately equals 60 \(\times\) 60 \(\times\) 6 images. Finally, for SVM the dataset is divided by ratio of 60:40 implying 60% images for training and 40% images for testing for both dataset sizes. For experiments, google collab has been used with the device type 'CPU' and memory limit of 268,435,456 to process the images.