Classification and Identification of Objects in Images Using CNN

Convolution neural network also called as CNN is one of the deep learning technique. CNN in recent time has evolved as most popular tool to solve vision related use cases. In the field of computer vision, the challenge of classifying a given image and detecting an object in an image is extremely difficult and it has numerous real-world applications. In recent years, the use of CNN has increased dramatically in a variety of fields, including image classification, segmentation, and object recognition. Alex Nets, GoogLeNet, and ResNet50 are the most popular CNNs for object detection and from the different images. The performance of CNN depends directly on its hyperparameters. More you tune those parameters better you get the results. As a result, it’s an important study on how to use CNN to improve object detection performance. Many strategies have been explored to optimise the hyperparameters of the CNN architecture. Gradient Descent, Back Propagation, Genetic Algorithm, Adam Optimization, and so on are some of them. The CNN architecture was trained using a variety of population-based search and evolutionary computing (EC) methodologies. Genetic algorithms, differential evolution, ant colony optimization, and particle swarm optimization, among other population-based techniques, have recently been utilised to train hyperparameters. In this literature, we will review the various aspects of CNN and its architecture followed by a detailed explanation of optimization strategies that aid in boosting accuracy.