## 4 Comparative Analysis of RPLC with CNN through Experimental Validation
### Benchmarking Analysis  
Following the discussed experimental setup, for the validation of RPLC we tested the classification accuracy against basic CNN, support vector machines (SVM). The proposed classification is backed by extensive experimentation for identification of luminosity thresholds based on the annotated dataset images in terms of Dark, Semi-dark and Bright images. After a lot of considerate and meticulously organized experimentation, we introduced the thresholds in form of luminosity spectrums for Dark, Semi-dark and Bright spectrums. The presented spectrums are 0-0.09 for Dark, 0.09-0.47 for Semi-dark, and 0.47-1 for Bright, as shown in Figure 4. For the basic CNN and SVM classification, the requirement is annotated data for classification startup. Whereas, for RPLC the only requirement is prior knowledge of predefined thresholds of each spectrum. The experiments for validation and benchmarking included two scenarios of different image dataset sizes. For the first scenario, where the dataset size under consideration was 300, Table 1 shows the comprehended accuracies and related logistics. Table 2 presents the accuracies obtained by basic CNN, SVM and luminosity based classification. For Basic CNN, the epochs used are 60, optimizer used is 'adagrad' and batch size is 15, whereas images sizes are kept 100 by 100 and 640 by 640. For SVM theoptimizer used is 'Linear' along with the images of sizes 100 by 100 and 640 by 640. For luminosity-based classification there are minimal requirements are imposed and image sizes considered for experiments are 100 by 100 and the actual image size. The accuracy obtained by the CNN is 73.3% for training and the validation accuracy is 64.4% for image sizes of 100 by 100 whereas it is clearly seen that training loss is 56% and validation loss of the network is 100%. Similarly, CNN performance for image sizes of 640 by 640 is not any better. The accuracy is 53% and validation accuracy is 71.1% along with the training loss of 83% and validation loss of 100%. The acquired accuracy cannot be trusted for the classification as the losses associated are high which means there is high probability of getting incorrect results. As these classifications can be part of sophisticated and crucial image based applications, we cannot allow this much high error rate to propagate further. The Classification accuracy attained by SVM is 72.5% for image sizes 100 by 100 and 73.3% for image sizes 640 by 640. The accuracy is better than CNN. However, annotations and a high training cost are involved with the usage of SVM. The accuracy obtained by RPLC is 78% for image size of 100 by 100 and the accuracy obtained for actual image size is 77%. The obtained accuracy deduces the algorithm is highly predictable and consistent in nature even for various image sizes. Table 3 presents the accuracies obtained for image dataset of size 900 images. With the similar CNN architecture and hyperparameters as used earlier, the accuracy turn out to be 40% training accuracy and validation accuracy of 70% of image size of 100 by 100. Classification accuracy for image size 640 by 640 is found to be 66.7% whereas validation accuracy turns out to be 75.5%. While the losses associated are again witnessed to have higher in numbers as compared to the accuracies obtained. Similar CNN architecture with added data augmentation features to increase the dataset size is trained to check the resultant accuracy and associated losses. The data augmentation involved rescaling, width shifting, height shifting, shear, zoom and horizontal flipping. The rescaling range is set to 1/255, width and height shifting are made up to 10% by setting the argument value to 0.1. Similarly, zooming and shearing of 10% is employed. Finally, horizontal flip is also employed in the augmentation process. This makes up to six different augmentation processes. Using this data augmentation scheme.